Miracle-dz/UniFAR-FTUs huggingface dataset:
The wisdom nexus: Guiding information systems research, practice, and education

“Wisdom” has been at the heart of practical, philosophical, and spiritual disciplines since antiquity and is now increasingly receiving organizational as well as academic attention in scholarly areas that include psychology and organizational studies among others. However, practical wisdom has not received adequate focus in the information systems (IS) discipline. In this commentary, we make the case for a systemic focus on the role of wisdom in IS research, teaching, and practice. We introduce underlying theoretical concepts of wisdom; review relevant key research linking wisdom and IS; propose a framework to link wisdom with IS research, practice, and education; and discuss the implications of the framework and illustrate how it can be used to generate research opportunities for IS. We conclude that wisdom, whose goal is the common good, can provide a foundation to discover, view, understand, and resolve myriad issues that lie at the nexus of wisdom and information technologies.
Public Participation Using 3D Web-Based City Models: Opportunities for E-Participation in Kisumu, Kenya

Public participation is significant for the success of any urban planning project. However, most members of the general public are not planning professionals and may not understand the technical details of a 2D paper-based plan, which might hamper their participation. One way to expand the participation of citizens is to present plans in well-designed, user-friendly and interactive platforms that allow participation regardless of the technical skills of the participants. This paper investigates the impacts of the combined use of 3D visualization and e-participation on public participation in Kisumu, Kenya. A 3D city model, created with CityEngine2016, was exported into a web-based geoportal and used as a Planning Support System in two stakeholder workshops in order to evaluate its usability. In order to assess the workshops 300 questionnaires were given out to planning practitioners and interview were done with key informants. Five indicators were developed for evaluating the usability of the 3D model while the usability of e-participation was evaluated using communication, collaboration and learning as indicators. Results showed that effectiveness and efficiency varied within different professional groups while the questionnaires showed strong preference for e-participation methods, especially Short Message Servicess/Unstructured Supplementary Service Data and emails. The study concludes that the use of 3D visualization and e-participation has the potential to improve the quality and quantity of public participation and recommends further research on the subject.
Contribution to the Study of Projection-based Systems for Industrial Applications in Mixed Reality. (Contribution à l'étude des systèmes de projection pour des applications industrielles en réalité mixte)

Mixed reality brings some advantages to industrial applications. It can, among others, facilitate visualizing and validating projects or assist operators during specific tasks. Projection-based Systems (PBS), such as CAVEs or spatial augmented reality provide a mixed reality environment enabling straightforward collaboration with external users. In this thesis, we aim at improving the usage of PBS for industrial applications by considering two main challenges: (1) improving the technical components of PBS and (2) improving the user experience when using PBS. As a first technical challenge, we propose to address the improvement of the tracking component. We introduce an approach that enables increasing the workspace of optical tracking systems by using two methods. As a first method, we propose to use monocular tracking. As a second method, we propose to use controlled cameras that follow the targets across the workspace. The resulting system provides acceptable performances for mixed reality applications while considerably increasing the workspace. Such a tracking system can make it easier to use large projection-based displays and can widen the range of available interactions. As a second technical challenge, we design an “all-in-one” headset for mobile spatial augmented reality on tangible objects. The headset gathers both a projection and a tracking system that are embedded on the user’s head. With such a system, the users are able to move around tangible objects and to manipulate them directly by hand while projecting virtual content over them. We illustrate our system with two industrial use cases: virtual prototyping and medical visualization. Finally, we address the challenge that aims at improving the user experience when using PBS. We introduce a method that provides virtual embodiment and increases the spatial perception of the users when using PBS. To do so we add the user’s virtual shadow in immersive projection-based systems. The virtual shadow is dynamically mapped to users’ movements in order to make them perceive the shadow as if it was their own. We then carry out an experiment to study the influence of the presence of the virtual shadow on the user experience and behavior.
From a Vegetation Index to a Sustainable Development Goal Indicator: Forest Trend Monitoring Using Three Decades of Earth Observations across Switzerland

Forests represent important habitats for species and provide multiple ecosystem services for human well-being. Preserving forests and other terrestrial ecosystems has become crucial to halt desertification, land degradation, and biodiversity loss worldwide, and is also one of the Sustainable Development Goals (SDGs) to be achieved by 2030. Remote sensing could greatly contribute to measuring progress toward SDGs by providing consistent and repetitive coverage of large areas, as well as information in various wavelengths, which facilitates the monitoring of environmental trends at various scales. This paper focuses on SDG indicator 15.1. 1—“Forest area as a percentage of total land area” to demonstrate the potential of Earth Observation Data Cubes for SDGs. The approach presented here uses Landsat Analysis Ready Data (ARD) stored in the Swiss Data Cube, and offers a complementary method to ground-based approaches to monitor Switzerland’s forest extent based on the Normalized Difference Vegetation Index (NDVI). The proposed method performs time-series analyses to extract a forest/non-forest map and a graph representing the trend of SDG 15.1. 1 indicator over time. Preliminary results suggest that this approach can identify similar forest extent and growth patterns to observed trends, and can therefore help monitor progress toward the selected SDG indicator more effectively.
Person-Independent Facial Expression Recognition with Histograms of Prominent Edge Directions

This paper presents a new descriptor, named Histograms of Prominent Edge Directions (HPED), for the recognition of facial expressions in a person-independent environment. In this paper, we raise the issue of sampling error in generating the code-histogram from spatial regions of the face image, as observed in the existing descriptors. HPED describes facial appearance changes based on the statistical distribution of the top two prominent edge directions (i.e., primary and secondary direction) captured over small spatial regions of the face. Compared to existing descriptors, HPED uses a smaller number of code-bins to describe the spatial regions, which helps avoid sampling error despite having fewer samples while preserving the valuable spatial information. In contrast to the existing Histogram of Oriented Gradients (HOG) that uses the histogram of the primary edge direction (i.e., gradient orientation) only, we additionally consider the histogram of the secondary edge direction, which provides more meaningful shape information related to the local texture. Experiments on popular facial expression datasets demonstrate the superior performance of the proposed HPED against existing descriptors in a person-independent environment.
Music retiler: Using NMF2D source separation for audio mosaicing

Musaicing (music mosaicing) aims at reconstructing a target music track by superimposing audio samples selected from a collection. This selection is based on their acoustic similarity to the target. The baseline technique to perform this is concatenative synthesis in which the superposition only occurs in time. Non-Negative Matrix Factorization has also been proposed for this task. In this, a target spectrogram is factorized into an activation matrix and a predefined basis matrix which represents the sample collection. The superposition therefore occurs in time and frequency. However, in both methods the samples used for the reconstruction represent isolated sources (such as bees) and remain unchanged during the musaicing (samples need to be pre-pitch-shifted). This reduces the applicability of these methods. We propose here a variation of the musaicing in which the samples used for the reconstruction are obtained by applying a NMF2D separation algorithm to a music collection (such as a collection of Reggae tracks). Using these separated samples, a second NMF2D algorithm is then used to automatically find the best transposition factors to represent the target. We performed an online perceptual experiment of our method which shows that it outperforms the NMF algorithm when the sources are polyphonic and multi-source.
Interactive Control of Explicit Musical Features in Generative LSTM-based Systems

Long Short-Term Memory (LSTM) neural networks have been effectively applied on learning and generating musical sequences, powered by sophisticated musical representations and integrations into other deep learning models. Deep neural networks, alongside LSTM-based systems, learn implicitly: given a sufficiently large amount of data, they transform information into high-level features that, however, do not relate with the high-level features perceived by humans. For instance, such models are able to compose music in the style of the Bach chorales, but they are not able to compose a less rhythmically dense version of them, or a Bach choral that begins with low and ends with high pitches -- even more so in an interactive way in real-time. This paper presents an approach to creating such systems. A very basic LSTM-based architecture is developed that can compose music that corresponds to user-provided values of rhythm density and pitch height/register. A small initial dataset is augmented to incorporate more intense variations of these two features and the system learns and generates music that not only reflects the style, but also (and most importantly) reflects the features that are explicitly given as input at each specific time. This system -- and future versions that will incorporate more advanced architectures and representation -- is suitable for generating music the features of which are defined in real-time and/or interactively.
Identification of Experimental and Control Areas for CCTV Effectiveness Assessment - The Issue of Spatially Aggregated Data

Progress in surveillance technology has led to the development of Closed-Circuit Television (CCTV) systems in cities around the world. Cameras are considered instrumental in crime reduction, yet existing research does not unambiguously answer the question whether installing them affects the number of crimes committed. The quasi-experimental method usually applied to evaluate CCTV systems’ effectiveness faces difficulties with data quantity and quality. Data quantity has a bearing on the number of crimes that can be conclusively inferred using the experimental procedure. Data quality affects the level of crime data aggregation. The lack of the exact location of a crime incident in the form of a street address or geographic coordinates hinders the selection procedure of experimental and control areas. In this paper we propose an innovative method of dealing with data limitations in a quasi-experimental study on the effectiveness of CCTV systems in Poland. As police data on crime incidents are geocoded onto a neighborhood or a street, we designed a method to overcome this drawback by applying similarity measures to time series and landscape metrics. The method makes it possible to determine experimental (test) and control areas which are necessary to conduct the study.
The technostress trifecta ‐ techno eustress, techno distress and design: Theoretical directions and an agenda for research

Technostress—defined as stress that individuals experience due to their use of Information Systems—represents an emerging phenomenon of scholarly investigation. It examines how and why the use of IS causes individuals to experience various demands that they find stressful. This paper develops a framework for guiding future research in technostress experienced by individuals in organizations. We first review and critically analyse the state of current research on technostress reported in journals from the IS discipline and the non‐IS disciplines that study stress in organizations (eg, organizational behaviour and psychological stress). We then develop our framework in the form of the “technostress trifecta”—techno‐eustress, techno‐distress, and Information Systems design principles for technostress. The paper challenges 3 key ideas imbued in the existing technostress literature. First, it develops the argument that, in contrast to negative outcomes, technostress can lead to positive outcomes such as greater effectiveness and innovation at work. Second, it suggests that instead of limiting the role of IS to that of being a stress creator in the technostress phenomenon, it should be expanded to that of enhancing the positive and mitigating the negative effects of technostress through appropriate design. Third, it lays the groundwork for guiding future research in technostress through an interdisciplinary framing that enriches both the IS and the psychological stress literatures through a potential discourse of disciplinary exchange.
3D anisotropic mesh adaptation for Reynolds Averaged Navier-Stokes simulations. (Adaptation de maillage anisotrope 3D pour les simulations RANS)

The fast and reliable simulation of turbulent flow using Reynolds Averaged Navier Stokes (RANS) models is a major financial issue for many industries. With the increasing complexity of geometries and simulated flows, as well as requirements in terms of fidelity, the generation of appropriate meshes has become a key link in the chain of computation. We show in this thesis the ability of modern numerical schemes to simulate turbulent flows on fully unstructured meshes generated automatically using mesh adaptation methods. We present the implementation of different versions of the Spalart-Allmaras model as well as the numerical choices guaranteeing a sufficient robustness of the solver in order to not require a structured boundary layer. We then introduce the error analysis necessary to propose different error estimators for mesh optimization. This methodology is tested on various external aerodynamic and turbomachinery test cases and compared to traditional mesh generation methods. We show the ability of mesh adaptation methods to automatically generate optimal mesh sizes for RANS simulations on realistic and complex geometries.
A Personalized Recommendation Algorithm Based on the User's Implicit Feedback in E-Commerce

A recommendation system can recommend items of interest to users. However, due to the scarcity of user rating data and the similarity of single ratings, the accuracy of traditional collaborative filtering algorithms (CF) is limited. Compared with user rating data, the user’s behavior log is easier to obtain and contains a large amount of implicit feedback information, such as the purchase behavior, comparison behavior, and sequences of items (item-sequences). In this paper, we proposed a personalized recommendation algorithm based on a user’s implicit feedback (BUIF). BUIF considers not only the user’s purchase behavior but also the user’s comparison behavior and item-sequences. We extracted the purchase behavior, comparison behavior, and item-sequences from the user’s behavior log; calculated the user’s similarity by purchase behavior and comparison behavior; and extended word-embedding to item-embedding to obtain the item’s similarity. Based on the above method, we built a secondary reordering model to generate the recommendation results for users. The results of the experiment on the JData dataset show that our algorithm shows better improvement in regard to recommendation accuracy over other CF algorithms.
Bayesian Rules Based Optimal Defense Strategies for Clustered WSNs

Considering the topology of hierarchical tree structure, each cluster in WSNs is faced with various attacks launched by malicious nodes, which include network eavesdropping, channel interference and data tampering. The existing intrusion detection algorithm does not take into consideration the resource constraints of cluster heads and sensor nodes. Due to application requirements, sensor nodes in WSNs are deployed with approximately uncorrelated security weights. In our study, a novel and versatile intrusion detection system (IDS) for the optimal defense strategy is primarily introduced. Given the flexibility that wireless communication provides, it is unreasonable to expect malicious nodes will demonstrate a fixed behavior over time. Instead, malicious nodes can dynamically update the attack strategy in response to the IDS in each game stage. Thus, a multi-stage intrusion detection game (MIDG) based on Bayesian rules is proposed. In order to formulate the solution of MIDG, an in-depth analysis on the Bayesian equilibrium is performed iteratively. Depending on the MIDG theoretical analysis, the optimal behaviors of rational attackers and defenders are derived and calculated accurately. The numerical experimental results validate the effectiveness and robustness of the proposed scheme.
Bi-dimensional Empirical Mode Decomposition Algorithm Based on Particle Swarm-Fractal Interpolation

Performance of the interpolation algorithm used in the technique of bi-dimensional empirical mode decomposition directly affects its popularization and application, so that the researchers pay more attention to the algorithm reasonable, accurate and fast. However, it has been a lack of an adaptive interpolation algorithm that is relatively satisfactory for the bi-dimensional empirical mode decomposition (BEMD) and is derived from the image characteristics. In view of this, this paper proposes an image interpolation algorithm based on the particle swarm and fractal. Its procedure includes: to analyze the given image by using the fractal brown function, to pick up the feature quantity from the image, and then to operate the adaptive image interpolation in terms of the obtained feature quantity. All parameters involved in the interpolation process are determined by using the particle swarm optimization algorithm. The presented interpolation algorithm can solve those problems of low efficiency and poor precision in the interpolation operation of bi-dimensional empirical mode decomposition and can also result in accurate and reliable bi-dimensional intrinsic modal functions with higher speed in the decomposition of the image. It lays the foundation for the further popularization and application of the bi-dimensional empirical mode decomposition algorithm.
Bidirectional Recurrent Neural Network Approach for Arabic Named Entity Recognition

Recurrent neural network (RNN) has achieved remarkable success in sequence labeling tasks with memory requirement. RNN can remember previous information of a sequence and can thus be used to solve natural language processing (NLP) tasks. Named entity recognition (NER) is a common task of NLP and can be considered a classification problem. We propose a bidirectional long short-term memory (LSTM) model for this entity recognition task of the Arabic text. The LSTM network can process sequences and relate to each part of it, which makes it useful for the NER task. Moreover, we use pre-trained word embedding to train the inputs that are fed into the LSTM network. The proposed model is evaluated on a popular dataset called “ANERcorp. ” Experimental results show that the model with word embedding achieves a high F-score measure of approximately 88.01%.
A Cooperative-game Approach to Share Acceptability and Rank Arguments

We deploy a game-theoretic approach for analysing the acceptability of arguments in a generic Abstract Argumentation Framework. The result is a ranking-based semantics, which sorts arguments from the most to the least acceptable. In the computation of such a ranking, we adopt the Shapley Value formula, since it is usually used to fairly distribute costs to several entities in coalitions (labelled sets of arguments in our case). Finally, we show that some well-known properties are satisfied by the ranked-semantics we designed, and we provide an example of how our approach works.
Saliency-based Rhythmic Coordination of Perceptual Predictions

Objects, shown explicitly or held in mind internally, compete for limited processing resources. Recent studies have demonstrated that attention samples locations and objects rhythmically. Interestingly, periodic sampling not only operates over objects in the same scene but also occurs for multiple perceptual predictions that are held in attention for incoming inputs. However, how the brain coordinates perceptual predictions that are endowed with different levels of bottom–up saliency information remains unclear. To address the issue, we used a fine-grained behavioral measurement to investigate the temporal dynamics of processing of high- and low-salient visual stimuli, which have equal possibility to occur within experimental blocks. We demonstrate that perceptual predictions associated with different levels of saliency are organized via a theta-band rhythmic course and are optimally processed in different phases within each theta-band cycle. Meanwhile, when the high- and low-salient stimuli are presented in separate blocks and thus not competing with each other, the periodic behavioral profile is no longer present. In summary, our findings suggest that attention samples and coordinates multiple perceptual predictions through a theta-band rhythm according to their relative saliency. Our results, in combination with previous studies, advocate the rhythmic nature of attentional process.
Human Sexuality and Its Problems

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