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The purpose of this study is to apply spatial theory to a review of the literature on activity-based working in higher education. Globally, the office concept of activity-based working (ABW) is increasingly implemented in higher education, and scholars contributed to developing empirical explanations of the effects of implementing ABW in higher education. However, the focus on theory building is limited, decreasing the predictability and the understanding of implementing ABW.

The authors developed a theoretical framework by categorizing the empirical findings of earlier accounts by integrating them with Lefebvre’s spatial theory. They conducted a systematic literature review of 21 studies published between 2008 and 2022 that reported on the phenomenon of ABW among higher-education employees.

It remains to be seen whether the implementation of the ABW in higher education is successful in terms of pre-defined goals. The studies investigating academic workplace concepts have led to inconsistent findings that lack an underlying framework. As the ABW concept fails to adequately support academics’ work processes, it is recommended that managers and architects consider their subjective perspectives about the use of space and take the time to understand the users’ fundamental values.

The authors integrated the selected studies with Lefebvre’s spatial theory, and this model includes three perspectives that can explain workers' experiences with ABW. This theoretical framework can assist researchers in gaining a deeper understanding of ABW and support practitioners in implementing it in higher education.

This study aims to investigate the differences in spatial ability between students with a math learning disability and their normal peers.

To investigate these differences two groups, (60 students with a math learning disability) and (60 normal students) from fifth grade with a mean age (10.6 years) were administered with spatial ability test along with an IQ test. Students with a math learning disability were chosen using measures of the following: math learning disability questionnaire developed from learning disability evaluation scale – renormed second edition (LDES-R²) (McCarney and Arthaud, 2007) and the Quick Neurological Screening Test (Mutti et al., 2012), in addition to their marks in formal math tests in school.

Comparison between the two groups in four aspects of spatial ability resulted in obvious differences in each aspect of spatial ability (spatial relations, mental rotation, spatial visualization and spatial orientation); these differences were clear, especially in mental rotation and spatial visualization.

This paper contributes to gain more insights into the characteristics of pupils with a math learning disability, the nature of spatial abilities and its effect on a math learning disability. Moreover, the results suggest spatial ability to be an important diagnose factor to distinguish and identify students with a math learning disability, and that spatial ability is strongly relevant to math achievement. The results have significant implications for success in the science, technology, engineering and mathematics domain.

In this paper, an emerging state-of-the-art machine intelligence technique called the Hierarchical Temporal Memory (HTM) is applied to the task of short-term load forecasting (STLF). A HTM Spatial Pooler (HTM-SP) stage is used to continually form sparse distributed representations (SDRs) from a univariate load time series data, a temporal aggregator is used to transform the SDRs into a sequential bivariate representation space and an overlap classifier makes temporal classifications from the bivariate SDRs through time. The comparative performance of HTM on several daily electrical load time series data including the Eunite competition dataset and the Polish power system dataset from 2002 to 2004 are presented. The robustness performance of HTM is also further validated using hourly load data from three more recent electricity markets. The results obtained from experimenting with the Eunite and Polish dataset indicated that HTM will perform better than the existing techniques reported in the literature. In general, the robustness test also shows that the error distribution performance of the proposed HTM technique is positively skewed for most of the years considered and with kurtosis values mostly lower than a base value of 3 indicating a reasonable level of outlier rejections.

The purpose of this paper is to develop a methodology for shaping the tourist spatial identity of the city and to take advantage of it to discover alternative urban outdoor spaces. As the number of indoor visitors has been limited due to the COVID-19 pandemic, open urban areas such as streets, squares and parks have become more important tourist locations.

The assessment methodology consists of two basic steps. In the first step, the authors look for places or points that are carriers of spatial identity. For this purpose, the method of mental mapping is used. In the second step, statistical methods are used to evaluate the spatial suitability for the most common tourist activities. To obtain a holistic picture, a temporal component is included.

The application of the methodology is presented in the form of a case study. The obtained research results provide an insight into the spatial situation of the city of Maribor (Slovenia, Europe). Tourist spatial identity of a city depends on time. Based on the value of spatial sensitivity indicator and the suitability of activities, it is possible to adapt the tourist offer to the temporal component.

To the best of the authors’ knowledge, this is an original perspective on the spatial identity of tourists. The presented approach could be integrated as a good practice in any other city worldwide. It supports the identification of suitable outdoor tourist places that are memorable, cosy, multifunctional and can be recommended by city guides (mobile or printed books). Every city has many hidden gems that tourists have yet to discover.

Representations of time are commonly used to construct narratives in visualisations of data. However, since time is a value-laden concept, and no representation can provide a full, objective account of “temporal reality”, they are also biased and political: reproducing and reinforcing certain views and values at the expense of alternative ones. This conceptual paper aims to explore expressions of temporal bias and politics in data visualisation, along with possibly mitigating user approaches and design strategies.

This study presents a theoretical framework rooted in a sociotechnical view of representations as biased and political, combined with perspectives from critical literacy, radical literacy and critical design. The framework provides a basis for discussion of various types and effects of temporal bias in visualisation. Empirical examples from previous research and public resources illustrate the arguments.

Four types of political effects of temporal bias in visualisations are presented, expressed as limitation of view, disregard of variation, oppression of social groups and misrepresentation of topic and suggest that appropriate critical and radical literacy approaches require users and designers to critique, contextualise, counter and cross beyond expressions of the same. Supporting critical design strategies involve the inclusion of multiple datasets and representations; broad access to flexible tools; and inclusive participation of marginalised groups.

The paper draws attention to a vital, yet little researched problem of temporal representation in visualisations of data. It offers a pioneering bridging of critical literacy, radical literacy and critical design and emphasises mutual rather than contradictory interests of the empirical sciences and humanities.

Perception has been identified as the main cause underlying most autonomous vehicle related accidents. As the key technology in perception, deep learning (DL) based computer vision models are generally considered to be black boxes due to poor interpretability. These have exacerbated user distrust and further forestalled their widespread deployment in practical usage. This paper aims to develop explainable DL models for autonomous driving by jointly predicting potential driving actions with corresponding explanations. The explainable DL models can not only boost user trust in autonomy but also serve as a diagnostic approach to identify any model deficiencies or limitations during the system development phase.

This paper proposes an explainable end-to-end autonomous driving system based on “Transformer,” a state-of-the-art self-attention (SA) based model. The model maps visual features from images collected by onboard cameras to guide potential driving actions with corresponding explanations, and aims to achieve soft attention over the image’s global features.

The results demonstrate the efficacy of the proposed model as it exhibits superior performance (in terms of correct prediction of actions and explanations) compared to the benchmark model by a significant margin with much lower computational cost on a public data set (BDD-OIA). From the ablation studies, the proposed SA module also outperforms other attention mechanisms in feature fusion and can generate meaningful representations for downstream prediction.

In the contexts of situational awareness and driver assistance, the proposed model can perform as a driving alarm system for both human-driven vehicles and autonomous vehicles because it is capable of quickly understanding/characterizing the environment and identifying any infeasible driving actions. In addition, the extra explanation head of the proposed model provides an extra channel for sanity checks to guarantee that the model learns the ideal causal relationships. This provision is critical in the development of autonomous systems.

Cognitive computing is part of AI and cognitive applications consists of cognitive services, which are building blocks of the cognitive systems. These applications mimic the human brain functions, for example, recognize the speaker, sense the tone of the text. On this paper, we present the similarities of these with human cognitive functions. We establish a framework which gathers cognitive functions into nine intentional processes from the substructures of the human brain. The framework, underpins human cognitive functions, and categorizes cognitive computing functions into the functional hierarchy, through which we present the functional similarities between cognitive service and human cognitive functions to illustrate what kind of functions are cognitive in the computing. The results from the comparison of the functional hierarchy of cognitive functions are consistent with cognitive computing literature. Thus, the functional hierarchy allows us to find the type of cognition and reach the comparability between the applications.

Considering the growing interests in managerial and accounting issues related to blockchain technology (BT), the study aims at identifying the main research venues in this specific field. In particular, the purpose is to understand the spatial and temporal production and distribution of research documents, highlighting the most relevant topics, the most influential authors and research.

This research carries out a bibliometric analysis of 189 research documents in the business, management and accounting areas. Data collection and refining is carried out from the Scopus database. The data analysis is based on a hybrid literature review approach using a descriptive bibliometric method, data analysis visualization (through VOSViewer software) and thematic analysis.

Results indicate that research studies focused on BT and accounting have been growing exponentially over the last three years, with authors who previously focused on generalist themes, and are now facing more specific issues. Through cluster analysis, the authors propose the framework of accounting domain and blockchain technology (ADOB) to systematize and visualize the map of current studies about the BT in the accounting domain.

The analysis highlights some aspects less investigated at the first research stage in the field of BT and accounting, such as the growing need of new accounting and control processes to address the practical issues of BT implementation and the need for education and training to stimulate a proper use of BT by accountants and practitioners.

This study is the first to adopt a bibliometric and thematic analysis to investigate BT in the accounting domain. The authors provide significant insights that could guide and foster the use of BT for accountants and practitioners, defining future research lines and a research agenda for academic researchers.

Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges.

The purpose of the paper is to predict mapping of areas vulnerable to flooding in the Ourika watershed in the High Atlas of Morocco with the aim of providing a useful tool capable of helping in the mitigation and management of floods in the associated region, as well as Morocco as a whole.

Four machine learning (ML) algorithms including k-nearest neighbors (KNN), artificial neural network, random forest (RF) and x-gradient boost (XGB) are adopted for modeling. Additionally, 16 predictors divided into categorical and numerical variables are used as inputs for modeling.

The results showed that RF and XGB were the best performing algorithms, with AUC scores of 99.1 and 99.2%, respectively. Conversely, KNN had the lowest predictive power, scoring 94.4%. Overall, the algorithms predicted that over 60% of the watershed was in the very low flood risk class, while the high flood risk class accounted for less than 15% of the area.

There are limited, if not non-existent studies on modeling using AI tools including ML in the region in predictive modeling of flooding, making this study intriguing.