Reviewerly/exHarmony huggingface dataset:
A qualitative assessment of the eResearch Knowledge Centre’s support practices in the Human Sciences Research Council in Pretoria, South Africa

Purpose This paper aims to assess whether the current eResearch Knowledge Centre’s (eRKC) research support practices align with researchers’ requirements for achieving their research objectives. The study’s objectives were to assess the current eRKC research support services and to determine which are adequate and which are not in supporting the Human Sciences Research Council (HSRC) researchers. Design/methodology/approach This study uses interviews as part of the qualitative approach. The researcher chose to use interviews, as some aspects warranted further explanation during the interview. The interviews were scheduled using Zoom’s scheduling assistant. The interviews were semi-structured, guided by a flexible interview procedure and supplemented by follow-up questions, probes and comments. The research life cycle questions guided the interviews. The data obtained were coded and transcribed using MS Excel. The interview data were analysed, using NVivo, according to the themes identified in the research questions and aligned with the theory behind the study. Pre-determined codes were created in line with the six stages of the research life cycle and applied to group the data and extract meaning from each category. Interviewee responses were assigned to groups in line with the stages of the research life cycle. Findings The current eRKC research support services are aligned with the needs of HSRC researchers and highlight services that could be expanded or promoted more effectively to HSRC researchers. It proposes a new service, data analysis, and suggests that the eRKC could play a more prominent role in research impact, research data management and fostering collaboration with HSRC research divisions. Research limitations/implications This study is limited to assessing the eRKC’s support practices at the HSRC in Pretoria, South Africa. A more comprehensive study is needed for HSRC research services, capabilities and capacity. Practical implications Assessment of eRKC followed a comprehensive interviewee schedule that followed Raju and Schoombee’s research life cycle model. Social implications Zoom’s scheduling assistant may have generated Zoom fatigue and reduced productivity. Technical issues, losing time, communication gaps and distant time zones may have affected face-to-face interaction. Originality/value eRKC research support practices are rare in South Africa and most parts of the world. This study bridges the gap between theory and practice in assessing eRKC research support practices.
A Tool for Control Research Using Evolutionary Algorithm That Generates Controllers with a Pre-Specified Morphology

In control research and design it is frequently necessary to explore, evaluate, tune and compare many control strategies. These activities are assisted by software tools of increasing complexity; however, even with the existing high performance tools these activities are very time consuming due to they imply hundred if not thousand of simulations. If the process of doing such simulations is not automated it can be a very time consuming task. There has been proposed evolutionary algorithms (EA) that in the search for an optimal control automatically generate many control structures. However, the space of possible controllers for any dynamical system is huge. Hence it is mandatory to restrict the search space. The best way to restrict the controller search space is to let the designer influence the search direction. In this paper we propose a software tool for control research that has as its main part an EA that produce only controllers having a pre-specified morphology. By specifying a controller morphology the designer can influence the search direction without losing the exploration capability of evolutionary algorithms. The EA is endowed with a cost function tailored for fast evaluation of closed-loop controller performance. The use of the tool is illustrated by searching an sliding mode and similar controllers for an unstable linear and two nonlinear systems.
Multidomain feature fusion method for small object classification: MDFF

The task of classifying small objects is still challenging for current deep learning classification models [such as convolutional neural networks (CNNs) and vision transformers (ViTs)]. We believe that these algorithms are not designed specifically for small targets, so their feature extraction abilities for small targets are insufficient. To improve the classification capabilities of CNN-based and ViT-based classification models for small objects, two multidomain feature fusion (MDFF) frameworks are proposed to increase the amount of feature information derived from images and they are called MDFF-ConvMixer and MDFF-ViT. Compared with the basic model, the uniquely added design includes frequency domain feature extraction and MDFF processes. In the frequency domain feature extraction part, the input image is first transformed into a frequency domain form through discrete cosine transform (DCT) transformation and then a three-dimensional matrix containing the frequency domain information is obtained via channel splicing and reshaping. In the MDFF part, MDFF-ConvMixer splices the spatial and frequency domain features by channel, whereas MDFF-ViT uses a cross-attention mechanism to fuse the spatial and frequency domain features. When targeting small target classification tasks, these two frameworks obviously improve the utilized classification algorithm. On the DOTA dataset and the CIFAR10 dataset with two downsampling operations, the accuracies of MDFF-ConvMixer relative to ConvMixer increase from 87.82% and 62.14% to 90.14% and 66.00%, respectively, and the accuracies of MDFF-ViT relative to the ViT increase from 79.22% and 36.2% to 88.15% and 59.23%, respectively.
Evaluation of a rule-based approach to automatic factual question generation using syntactic and semantic analysis

We present a rule-based approach to automatic factual question generation implemented in the Adaptive Courseware and Natural Language Tutor, a natural language-based intelligent tutoring system. Since machine-generated questions are intended for adaptive teaching, learning and assessment, their accuracy is of the utmost importance. However, the generation of high-quality questions is still challenging. The proposed approach relies on pre-processing techniques and syntactic and semantic feature extraction to transform declarative sentences and their segments into questions. The quality of questions, generated from domain specific texts, was evaluated by using mixed evaluation strategies: (1) human evaluation, (2) qualitative error analysis, (3) automatic evaluation, (4) human and automatic evaluation of machine-generated questions from paraphrases compared to a set of human-authored questions, (5) preliminary comparison to other approaches. The human evaluation involved two teachers of English as a foreign language who set up evaluation criteria (grammaticality, semantic accuracy, and answerability) and a group of 30 English language graduates. Student-generated questions were validated and used as reference questions for automatic evaluation based on similarity metrics (BLEU-4, METEOR, CHRF, NIST and ROUGE-L). Human and automatic evaluation results were satisfactory but improved significantly with the paraphrasing strategy. The preliminary comparison to other approaches showed that the proposed rule-based approach performed equally well despite its limitations.
CRIMINALITY DATA SCRUTINY USING LOGISTIC REGRESSION ALGORITHM

The quantity of criminal cases in India is rising rapidly, which is the reason there are likewise a rising number of cases as yet extraordinary. Criminal cases are expanding ceaselessly, making it difficult to sort and determine them. It's essential to perceive an area's patterns of crime to prevent it from working out. On the off chance that the specialists entrusted with researching violations have a strong comprehension of the patterns in crime happening in a specific area, they will actually want to improve. Finding the examples of crime in a particular area should be possible by applying AI and different calculations. This review predicts the sorts of wrongdoings that will happen in a given area utilizing wrongdoing information, which facilitates the characterization of criminal cases and considers suitable activity. This exploration utilizes information from the most recent 18 years that were assembled from various solid sources. This article utilized choice, erasing invalid qualities, and mark encoding to clean and support the information since information pre-handling is similarly just about as vital as definite expectation. A compelling AI model for estimating the resulting criminal case is given by this exploration.
From Epistemic Norms to Logical Rules: Epistemic Models for Logical Expressivists

Abstract In this paper I construct a system of semantics for classical and intuitionistic propositional logic based on epistemic norms governing belief expansion. Working in the AGM-framework of belief change, I give a generalisation of Gärdenfors’ notion of belief systems which can be defined without reference to a logical consequence operator by using a version of the Ramsey Test. These belief expansion systems can then be used to define epistemic models which are sound and complete for either classical or intuitionistic propositional logic depending on which of the two notions of epistemic validity, identified by Levi and Arló-Costa, is used. Finally, I offer a discussion on how these results can be understood as providing a model theory within the framework of logical expressivism.
Lessons Learned during COVID-19 and Future Perspectives for Emerging Technology

The COVID-19 pandemic brought unprecedented challenges to the education sector, forcing schools at a worldwide level to quickly adapt their activities to remote learning. Despite the obstacles and challenges, the pandemic also represented an opportunity for reflection and innovation in education. A survey with 558 teachers from primary and middle schools in several regions of Italy was carried out to analyse challenges and lessons learned by Italian schools, aiming to improve the quality of digital education. The lessons learned highlighted the importance of developing strategies to address challenges such as the necessary infrastructure, digital skills, student engagement, collaboration, and personalised online learning. On the one hand, government-initiated interventions, like the National Recovery and Resilience Plan, aim to bridge the digital divide and improve education quality. On the other hand, the potentialities of immersive technologies like the Metaverse can provide exciting opportunities for interactive and engaging learning experiences, encouraging interaction and collaboration among students.
Critical Immersive-Triggered Literacy as a Key Component for Inclusive Digital Education

The present empirical study investigates, analyzes, and discusses the affordances and limitations of an augmented reality (AR)-based educational intervention for the critical digital awareness of secondary school students through a sociocultural framework of instruction. The design of the educational intervention focuses on the empowerment of students’ critical digital skills in the new media produsage ecosystem in parallel with the development of target language skills through content and language integrated learning (CLIL) in a mobile augmented reality (MAR)-based transnational educational setting. This study focuses on examining the usefulness rather than the digital use of MAR from a socio-constructivism perspective with 77 participants from diverse socio-cultural backgrounds and educational settings. More specifically, an investigation of the potential of internalization of new knowledge through immersive-triggered inclusive educational practices is carried out, examining the effectiveness of the universal design for learning principles (UDL) as a pedagogical framework for AR-based instruction related to long-term memory retention, the synthesis of meaningful learning instances, and the creation of new knowledge. The study findings suggest that AR-based instruction, if incorporated into a robust pedagogical framework, can enhance attention and long-term memory retention, provide meaningful, inclusive learning opportunities, and facilitate digital well-being in the ever-evolving complex learning ecosystem. This study concludes by proposing the term of critical immersive-triggered literacy (CIT Literacy), defined as a skill development framework that triggers learners’ attention and facilitates digital well-being for meaningful learning instances via immersive technologies.
MUS3E: A Mobility Ubiquitous Sensor Edge Environment for the Elderly

With the ageing of society, the number of households with older individuals or couples living alone is increasing. An “ageing-in-place” approach allows older adults to continue to live at home and receive help only when needed. However, this approach is insufficient for emergencies, such as falls, as well as for individuals with gradually deteriorating health conditions, such as frailty. Unexpected accidents significantly reduce the quality of life (QoL) of older adults. This paper proposes a new framework, the mobility ubiquitous sensor edge environment (MUS3E), to digitally transform ordinary houses to detect the movement of older individuals throughout their home environment and to notify family members and care providers of residents’ health status and safety information. The framework can be easily and inexpensively installed in any home, transforming an ordinary house into a smart home with an automated function for monitoring older residents. It uses ambient sensors such as passive infrared ray sensors to automatically measure health conditions by measuring factors such as walking speed. Residents need not interact with or control the system and can go about their daily lives. Since the sensors used in this system are mass-produced consumer products, they are inexpensive and easily replaceable, as there are many alternatives. In this study, we were able to demonstrate the practically and feasibility of this framework using a prototype that uses open architecture Internet of Things (IoT) software (Debian GNU/Linux 11, Arduino 1.8.19, ESP8266 2.7.4, ESP32 1.0.6, PubSubClient 2.8.0, ESPPerfectTime 0.3.0, mosquitto 2.0.11) components to digitally transform the living environment of older individuals.
MICROLEARNING FOR GENERATION Z IN THE FOREIGN LANGUAGE CLASSROOM

1 Financial University under the Government of the Russian Federation (RUSSIAN FEDERATION)2 Moscow Aviation Institute (National Research University) (RUSSIAN FEDERATION)3 Tomsk State University of Control Systems and Radioelectronics (RUSSIAN FEDERATION)
SDN-IoT: SDN-based efficient clustering scheme for IoT using improved Sailfish optimization algorithm

The Internet of Things (IoT) includes billions of different devices and various applications that generate a huge amount of data. Due to inherent resource limitations, reliable and robust data transmission for a huge number of heterogenous devices is one of the most critical issues for IoT. Therefore, cluster-based data transmission is appropriate for IoT applications as it promotes network lifetime and scalability. On the other hand, Software Defined Network (SDN) architecture improves flexibility and makes the IoT respond appropriately to the heterogeneity. This article proposes an SDN-based efficient clustering scheme for IoT using the Improved Sailfish optimization (ISFO) algorithm. In the proposed model, clustering of IoT devices is performed using the ISFO model and the model is installed on the SDN controller to manage the Cluster Head (CH) nodes of IoT devices. The performance evaluation of the proposed model was performed based on two scenarios with 150 and 300 nodes. The results show that for 150 nodes ISFO model in comparison with LEACH, LEACH-E reduced energy consumption by about 21.42% and 17.28%. For 300 ISFO nodes compared to LEACH, LEACH-E reduced energy consumption by about 37.84% and 27.23%.
Conditional probability and probability updating

Abstract The conditional probability formula is supposed to reflect the correct updating of probability assignments when new information is incorporated. Starting from a non-atomic probability measure, it is proved that the conditional probability formula provides the only transformed probability measure satisfying a “minimum requirement” relational assumption. This result applies to the standard Bayesian parametric model.
A Novel Edge-Inspired Depth Quality Evaluation Network for RGB-D Salient Object Detection

Recently, the pair of RGB images and depth images, which is denoted as RGB-D images, are introduced to improve the performances of salient object detection, because one of the pairs may stand out at least in one modality. However, most existing methods still suffer from dilemma. Firstly, the edges of predicted salient object are blurry. Secondly, how to integrate RGB images and depth images effectively still needs be explored. Thirdly, the quality of depth images have a strong impact on the performance of salient object detection so that the selection of depth images is worthy of exploring. To address above problems, we propose an edge-inspired depth quality evaluation network, which evaluates the quality of the depth images based on the edge information. More specifically, the depth quality evaluation module includes two parts: the depth decider and the depth aggregator. The former judges the quality of the depth images while the latter produces the weighted depth features. Then, the edge detection module is proposed to predict edges of salient object and produce edge features. In addition, features from VGG backbone, edge features and depth features are integrated by multi-modality feature fusion module, which is composed of a series of hybrid dilated convolutions. Moreover, the integrated features are fused by three-feature interactive module and double-feature interactive module to predict the final salient map. Our experiments on four RGB-D datasets demonstrate that our proposed method outperforms previous high-performance RGB-D salient object detection.
Digital Divide and Bridges: Navigating COVID-19 in Colombian Education

During the COVID-19 pandemic, educational institutions in Colombia implemented technology-mediated distance education models. However, inequities in access were evident. In Colombia, the digital divide is related to the geography, socioeconomic class, and ethnicity. The Ministry of National Education and the country’s educational communities joined efforts to build bridges and continue educating the population despite the difficulties. This exploratory qualitative research project was developed to reflect upon the effects of the pandemic on Colombian education, focussing upon the digital divide and the strategies implemented to reduce it. According to this study, the best way to bridge the digital divide is to mix older communication modalities and forms with new technology. During COVID-19, the main strategies used in Colombia involved the development of work guides or pamphlets; educational content on radio, television, and social media; digital educational resources accessible online; and the cell phone for direct contact. It is concluded that innovative bridges can be built to reduce the digital gap but only by embracing mediums of communication from across history.
Chain rules for a mutual information based on Rényi zero-relative entropy

Quantum R\'enyi relative entropies play a significant part in characterizing operational tasks in quantum information theory. In this paper, we first give some fundamental properties of the zero-relative entropy, and we find that the geometric R\'enyi relative entropy reduces to the zero-relative entropy in the limit case. Then, we define a new mutual information via the zero-relative entropy, and it is the so-called zero-mutual information. In particular, the zero-mutual information is a lower bound of the Petz-R\'enyi and geometric generalized mutual information. We establish the chain rules for the unsmoothed and smoothed versions of the zero-mutual information. As an application, we discuss generalized mutual information with the Petz-R\'enyi, sandwiched R\'enyi, and geometric R\'enyi types, our result gives a uniform chain rule inequality for quantum generalized mutual information.
Unsupervised image segmentation with robust virtual class contrast

Unsupervised Image Segmentation (UIS) is a challenging problem in computer vision that aims to classify pixels in an image into different semantic classes without using any labels. Among the various UIS methods, MaskContrast performs well when dealing with complex objects containing lots of detail. It utilizes prior knowledge from a pretrained saliency detection model to identify foreground pixels and leverages contrastive learning to learn the representations of those foreground pixels. However, this method is not designed to handle situations where foreground pixels belong to multiple semantic classes. To mitigate this problem, we propose a novel method for UIS called Robust Virtual Class Contrast (RVCC), which characterizes foreground pixels based on their “virtual classes” rather than just saliency masks. A virtual class can be viewed as the representation of some unknown object in the training data. Our method employs K-means clustering to find the pseudo virtual class label for every foreground pixel at the start of each training epoch. These pseudo-labels are then used to guide the learning of pixel representations. Additionally, to avoid the overconfidence of pseudo-label prediction, RVCC incorporates an additional regularization term that encourages consistency between the predictions under weak and strong augmentations. Our experiments demonstrate that RVCC outperforms existing baselines on the Pascal VOC2012 and MSRCv2 datasets, showcasing its capability for the UIS problem.
Understanding transitions in animated bar charts

Abstract Animated bar charts have gained popularity on the Internet for their ability to display historical rankings through smooth animated transitions that clearly show the rankings over time. To investigate the usage scenarios of these charts, we analyzed the pop-up comments to understand viewers’ focus. Our findings suggest that animated transitions play a key role in viewers’ perception of ranking changes. We summarized various transition methods and evaluated their impact on user perception and memory through user experiments. We also conducted a detailed analysis of each transition method’s implementation. Our research sheds light on the effectiveness of different transition strategies for animated bar charts.