Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the Influence of ICT in Development: A Study of Uganda’s Water Sector

In Sub-Saharan Africa, numerous Information and Communication Technology (ICT) initiatives have been implemented to address information gaps, service delivery and enhance decision-making within multiple sectors. Drawing on concomitant perceptions that ICT for Development (ICT4D) and Information Systems in Developing Countries (ISDC) studies postulate ICTs amplifying development conditions; this paper examines the influence of a development-oriented intervention implemented by Uganda’s Ministry of Water and Environment (MWE) to manage information challenges. The artefact called Water Supply Database (WSDB) is now positioned by MWE as the main information repository, and we describe the conceptual process of understanding and theorising its influence. This exploratory study adopts Classic Grounded Theory (CGT) to determine how stakeholders in the water sector perceive developmental influence of the WSDB. We present theoretical findings analysed from qualitative interviews to rationalize WSDB influence on water sector development and the implications of adopting CGT as a suitable methodology for theorisation in ICT4D.
Usability as Critical Success Factor of Mobile App for Pesticides Authenticity Verification: Reducing Risks for Farmers in Tanzania

Every year many accidents happen, the environment is damaged and financial resources are lost, particularly by smallholder farmers, due to the use of counterfeit pesticides products. Authorities, who are controlling the sale and use of those products, often struggle to keep up with the circulation of counterfeit products. A new electronic pesticides authenticity verification system, replacing a paper-based system, was recently developed. It includes a smartphone app, as main interface for smallholder farmers and inspectors of the pesticides regulatory authority. We present results of formative and summative usability tests, performed on the mobile application. Usability of a software product indicates to what extent the software is acceptable and usable by the target audience. The tests were done with the two main groups of pesticides stakeholders involved in buying and inspecting pesticides quality, the farmers and pesticides inspectors. Both user groups managed to perform all the scheduled tasks within the benchmarked time. In formative as well as summative usability tests the System Usability Scale (SUS) scores exceeded the average. Adoption of the app by the pesticides regulatory authority in Tanzania promises an increase in safety for humans and environment. The current app can be further improved by replacing numerical data entry by the users with image recognition to identify product specific numerical identifiers.
A Case Study of WiFi Sniffing Performance Evaluation

Mobile devices regularly broadcast WiFi probe requests in order to discover available proximal WiFi access points for connection. A probe request, sent automatically in the active scanning mode, consisting of the MAC address of the device expresses an advertisement of its presence. A real-time wireless sniffing system is able to sense WiFi packets and analyse wireless traffic. This provides an opportunity to obtain insights into the interaction between the humans carrying the mobile devices and the environment. Susceptibility to loss of the wireless data transmission is an important limitation on this idea, and this is complicated by the lack of a standard specification for real deployment of WiFi sniffers. In this paper, we present an experimental analysis of sniffing performance under different wireless environments using off-the-shelf products. Our objective is to identify the possible factors including channel settings and access point configurations that affect sniffing behaviours and performances, thereby enabling the design of a protocol for a WiFi sniffing system under the optimal monitoring strategy in a real deployment. Our preliminary results show that four main factors affect the sniffing performance: the number of access points and their corresponding operating channels, the signal strength of the access point and the number of devices in the vicinity. In terms of a real field deployment, we propose assignment of one sniffing device to each specific sub-region based on the local access point signal strength and coverage area and fixing the monitoring channel belongs to the local strongest access point.
A Predictive Energy Management System for a Hybrid Diesel-Electric Marine Propulsion Plant

An energy management control scheme using Non-linear Model Predictive Control (NMPC) is proposed for the optimal power-spit problem of a hybrid diesel-electric marine propulsion plant. The NMPC controller directly regulates the torque setpoints of the diesel engine and the electric motor/generator ensuring that certain constraints concerning the engine overloading are applied. The modeling for the controller design was based on experimental data gathered from the hybrid plant and on first principles for the diesel engine behavior and battery charging. The controller was experimentally tested in real-time operation. Results showed that the controller rejected successfully load disturbances and followed the rotational speed reference of the powertrain as well as the desirable state of charge in battery, in respect to the constraints of the system.
Pointing Direction Estimation for Attention Target Extraction Using Body-mounted Camera

In this paper, we propose a pointing-directionestimation method using a body-mounted camera. The opportunities to capture a large amount of image data in daily life are increasing due to the spread of smartphones and wearable cameras. In order to efficiently look back at the captured images, we aim to extract attention targets from the image sequences because the attention target is important for reminding people of their memories. Toward this purpose, in this paper, we propose a method for estimating the pointing direction from wearable camera images. The proposed method consists of two steps: arm skeleton estimation and pointing direction estimation. We model three types of pointing-directionestimation models and compare the estimations’ accuracy for evaluating which parts are important for pointing direction estimation. The experimental results show that the model based on the wrists and elbows had the best results.
Context-Based Forwarding for Mobile ICNs

Over the last couple of decades, mobile ad-hoc networks (MANETs) have been at the forefront of research, yet still are afflicted by high network fragmentation, due to their continuous node mobility and geographical dispersion. To address these concerns, a new paradigm was proposed, Information-Centric Networks (ICN), whose focus is the delivery of Content based on names. This article aims to use ICN concepts towards the delivery of both urgent and non-urgent information in urban mobile environments. In order to do so, a context-based forwarding strategy was proposed, with a very clear goal: to take advantage of both packet Names and Data, and node's neighborhood analysis in order to successfully deliver content into the network in the shortest period of time, and without worsening network congestion. The design, implementation and validation of the proposed strategy was performed using the ndnSIM platform along with real mobility traces from communication infrastructure of the Porto city. The results show that the proposed context-based forwarding strategy presents a clear improvement regarding the Data resolution, while maintaining network overhead at a constant.
Synchronization of multi-agent systems with directed topologies and heterogeneous delays

An algorithm for synchronization of a linear multi-agent system with directed topology is derived. Time delays in the control loop are taken into account when designing the control, these delays need not be equal for all agents. The Lyapunov-Krasovskii functional is used, the algorithm is formulated by means of linear matrix inequalities. It is shown that different values of the time delay can lead to an error in synchronization that does not converge to zero. A bound on this error is derived. Examples are presented to illustrate the proposed algorithm.
Impact of the Base Resistance Noise and Design of a −190-dBc/Hz FoM Bipolar Class-C VCO

This letter presents the analysis of phase noise generated from the base resistance in the bipolar transistor Class-C oscillator. It is found that the efficient pulsed current of transistors enhances this noise contribution. Moreover, the impact of the base resistance noise rises quickly with the oscillation amplitude, limiting the benefits of rising the supply voltage to reduce phase noise and penalizing the Figure of Merit (FoM). Furthermore, the amplitude stability problem is addressed and a simple expression for the maximum tail capacitance to prevent instability is derived. The phase noise analysis is validated by the measurements on a 12-GHz Class-C HBT VCO. With 3.4-V supply, the VCO reaches −187-dBc/Hz FoM, limited by the base resistance noise. The impact of this noise source is reduced by decreasing the supply voltage, and with 1.8-V supply, the VCO maintains a state-of-the-art phase noise (−120 dBc/Hz at 1 MHz) with an excellent −190-dBc/Hz FoM.
Research on Topology Planning for Wireless Mesh Networks Based on Deep Reinforcement Learning

Focus on the access point deployment and topology control problem in Wireless Mesh Networks (WMNs), a topology planning method based on deep reinforcement learning was proposed. Developing a method of sample data generation using monte-carlo tree search and self-game, then a policy and value network based on residual network was established. A model based on Tensorflow was developed to solve the training problem. Finally, simulation results show that the proposed method can provide efficient network planning solution with high performance on timeliness and validity.
Quadratic Stabilization of Bilinear Control Systems Subjected to Exogenous Disturbances*

We consider the design problem for bilinear control systems subjected to arbitrary bounded exogenous disturbances. A procedure for the construction of the stabilizability ellipsoid and domain of stabilizability for bilinear control systems is proposed and its efficiency is proved. The main tools are the linear matrix inequality technique and the apparatus of quadratic Lyapunov functions. This simple yet general approach is of great potential; for instance, it can be generalized to the discrete-time case, and to the various robust statements of the problem.
Analysis of Tapered Nanopillars for Reflective Metalens: The Role of Higher-Order Modes

Despite the widespread interest in the metalens technology, few works show the errors during fabrication and analyze how they influence the focusing performance. In this work, we proposed a reflective metalens design and carry out a fundamental study on how a mild tapering angle (<6°) of the nanopillars would influence the performance of a reflective metalens. By analyzing the interaction of Mie resonance in the lateral direction and Fabry-Pérot resonance in the longitudinal direction using numerical simulations, we reveal the detrimental role the tapering angle plays due to the low transmission near the resonance. We observe the weakening of the electric quadrupole and magnetic dipole response, and the increase of the electric dipole response for tapered nanopillars. Moreover, the Fabry-Pérot resonance is disturbed due to the broken symmetry. These factors contribute to lower Q factor resonance and hence the low transmission. The results presented in this work can guide the analysis of fabrication errors and provide insights on how to compensate them.
Efficient Novelty Search Through Deep Reinforcement Learning

Novelty search, which was inspired by the nature that evolves creatures with diversity, has shown great potential in solving reinforcement learning (RL) tasks with sparse and deceptive rewards. However, most of the existing novelty search methods evolve the populations through hybrization and mutation, which is inefficient in diverging populations. In this paper, we propose a method which incorporates deep RL with novelty search to improve the efficiency of diverging the populations for novelty search. We first propose a strategy that improves the novelty of individuals generated by genetic algorithm using reinforcement learning. Based on this strategy, we propose a framework that incorporates deep RL with novelty search, and then derive an algorithm to improve the search efficiency of the novelty search for continuous control tasks. Our experimental results show that our method can improve the search efficiency of novelty search and can also provide a competitive performance compared to some of the existing novelty search methods. The implementation of our method is available at: https://github.com/shilx001/NoveltySearch_Improvement.
A Skin Lesion Semi-supervised Segmentation Method

The skin is an essential system for the human body, also the most susceptible to diseases. The diagnosis of these diseases is made mainly through dermatoscopy images, a noninvasive procedure. This paper presents a semi-automatic method that can assist the physician in monitoring the skin lesion's evolution. Our approach applies the SLICO algorithm for superpixels generation, and texture features extraction. Subsequently, the algorithm Seeded Fuzzy C-means clusters the superpixels based on marked regions made by a specialist. Finally, the images undergo a post-processing step, eliminating noise and smoothing edges. We used 1380 images from four public datasets: the PH2 and DermIs to settle the method parameters, and ISIC 2016 and ISIC 2017 to the performance evaluation. Our tests show that by marking 1% of the superpixels, our method achieves an accuracy of 96.16% in ISIC 2016 and 95.34% in ISIC 2017. According to the literature, the segmentation was considered excellent in 93.49% of the images in ISIC 2016 and on 79.44% of ISIC 2017 images.
A Two-Stage Real-Time Path Planning: Application to the Overtaking Manuever

This paper proposes a two-stage local path planning approach to deal with all kinds of scenarios (i.e. intersections, turns, roundabouts). The first stage carries out an off-line optimization, considering vehicle kinematics and road constraints. The second stage includes all dynamic obstacles in the scene, generating a continuous path in real-time. Human-like driving style is provided by evaluating the sharpness of the road bends and the available space among them, optimizing the drivable area. The proposed approach is validated on overtaking scenarios where real-time path planning generation plays a key role. Simulation and real results on an experimental automated platform provide encouraging results, generating real-time collision-free paths while maintaining the defined smoothness criteria.
A Random Walk based Load Balancing Algorithm for Fog Computing

The growth of large scale sensing applications (as in the case of smart cities applications) is a main driver of the fog computing paradigm. However, as the load for such fog infrastructures increases, there is a growing need for coordination mechanisms that can provide load balancing. The problem is exacerbated by local overload that may occur due to an uneven distribution of processing tasks (jobs) over the infrastructure, which is typical real application such as smart cities, where the sensor deployment is irregular and the workload intensity can fluctuate due to rush hours and users behavior. In this paper we introduce two load sharing mechanisms that aim to offload jobs towards the neighboring nodes. We evaluate the performance of such algorithms in a realistic environment that is based on a real application for monitoring in a smart city. Our experiments demonstrate that even a simple load balancing scheme is effective in addressing local hot spots that would arise in a non-collaborative fog infrastructure.
Synchronization Solution to Optimize Power Consumption in Linear Sensor Network

Many organizations plan to collect remote data to perform incidents prediction, or just to store or analyze them. To gather some data, it is necessary to set up a sensor architecture. In this paper, we address several issues we have to consider when we design a dynamic wireless sensor network architecture aiming to collect and gather distributed data. We propose an original solution to the possible lack of an operator infrastructure covering the sensor deployment area. This solution also takes into account the communication capabilities of the different sensors, as well as the energy constraint. The dynamic management protocol we propose to combine a local synchronization solution, and a relay protocol. The efficiency of our solution, which can be applied to any wireless protocol, is shown by the results of a simulation campaign.
Parameterizing Variational Methods Through Data-Driven Inverse Problems for Image Processing Applications

The development of techniques to automatic set up parameters in image processing methods based on variational approaches is cumbersome because the model sensitivity to parameters values is not known in general. In this paper, we address this issue through a data-driven inverse problem. Specifically, our methodology receives pairs (input image(s), desired result(s)) and seeks for the near optimum parameter vector through an inverse problem based on minimization scheme. The methodology is not restricted to a particular functional and it does not require large annotated data sets as input. Besides, methods based on partial differential equations (PDEs) can be also accommodated in our approach. We validate the methodology for calibrating parameters using as test-bed a variation of Mumford-Shah method. The obtained solutions using the parameters found in this paper are compared with literature results in order to show the efficiency of our technique.
An In-Comparator Aperture-Time Equalization in a 7-nm FinFET CMOS 40-Gb/s Receiver

This letter proposes an in-comparator aperture-time equalization scheme using the impulse response of a clocked comparator. The technique is applied in a wireline link receiver prototype, implemented in CMOS 7-nm FinFET technology. The proposed method controls the aperture properties of the slicers by shaping their impulse sensitivity functions. We demonstrate an aperture skew control range of 4.7 ps with 147-fs accuracy for NRZ signaling at 40 Gb/s. PAM4 signaling at 80 Gb/s is also showcased using the proposed technique. These results serve as a proof of concept for next-generation source-synchronous chip-to-chip dense I/O links where aperture-time skews could be fine adjusted inside each comparator.
Hierarchical Load Balancing and Clustering Technique for Home Edge Computing

The edge computing system attracts much more attention and is expected to satisfy ultra-low response time required by emerging IoT applications. Nevertheless, as there were problems on latency such as the emerging traffic requiring very sensitive delay, a new Edge Computing system architecture, namely Home Edge Computing (HEC) supporting these real-time applications has been proposed. HEC is a three-layer architecture made up of HEC servers, which are very close to users, Multi-access Edge Computing (MEC) servers and the central cloud. This paper proposes a solution to solve the problems of latency on HEC servers caused by their limited resources. The increase in the traffic rate creates a long queue on these servers, i.e., a raise in the processing time (delay) for requests. By leveraging, based on clustering and load balancing techniques, we propose a new technique called HEC-Clustering Balance. It allows us to distribute the requests hierarchically on the HEC clusters and another focus of the architecture to avoid congestion on a HEC server to reduce the latency. The results show that HEC-Clustering Balance is more efficient than baseline clustering and load balancing techniques. Thus, compared to the HEC architecture, we reduce the processing time on the HEC servers to 19% and 73% respectively on two experimental scenarios.
Impact of Stemming and Word Embedding on Deep Learning-Based Arabic Text Categorization

Document classification is a classical problem in information retrieval, and plays an important role in a variety of applications. Automatic document classification can be defined as content-based assignment of one or more predefined categories to documents. Many algorithms have been proposed and implemented to solve this problem in general, however, classifying Arabic documents is lagging behind similar works in other languages. In this paper, we present seven deep learning-based algorithms to classify the Arabic documents. These are: Convolutional Neural Network (CNN), CNN-LSTM (LSTM = Long Short-Term Memory), CNN-GRU (GRU = Gated Recurrent Units), BiLSTM (Bidirectional LSTM), BiGRU, Att-LSTM (Attention-based LSTM), and Att-GRU. And for word representation, we applied the word embedding technique (Word2Vec). We tested our approach on two large datasets–with six and eight categories–using ten-fold cross-validation. Our objective was to study how the classification is affected by the stemming strategies and word embedding. First, we looked into the effects of different stemming algorithms on the document classification with different deep learning models. We experimented with eleven different stemming algorithms, broadly falling into: root-based and stem-based, and no stemming. We performed ANOVA test on the classification results using the different stemmers, which helps assure if the results are significant. The results of our study indicate that stem-based algorithms perform slightly better compared to root-based algorithms. Among the deep learning models, the Attention mechanism and the Bidirectional learning gave outstanding performance with Arabic text categorization. Our best performance is $F\text {-score} = 97.96\%$ , achieved using the Att-GRU model with stem-based algorithm. Next, we looked into different controlling parameters for word embedding. For Word2Vec, both skip-gram and bag-of-words (CBOW) perform well with either stemming strategies. However, when using a stem-based algorithm, skip-gram achieves good results with a vector of smaller dimension, while CBOW requires a larger dimension vector to achieve a similar performance.
Towards Small-Scale Farmers Fair Credit Scoring Technique

In consumer loans, where lenders deal with masses, use of algorithms to classify borrowers is fast catching up. Classification based on predictive models tend, to adversely affect borrowers. In this paper, we study the extent to which various algorithms disenfranchise borrowers lying on the boundaries of decision making. In the study, the data used for loan appraisal, and decisions made by the lenders are subjected to a set of select algorithms. The bias suffered by borrowers in each case is determined using mean absolute error (MAE) and relative absolute error (RAE). The results show that FURIA has the least bias with the MAE of 0.2662 and 0.1501 and RAE of 64.19% and 30.31% for the German and Australian data sets respectively. Consequently, FURIA is modified to remove the hard boundaries which results in even lower MAE of 0.2535 and 0.1264 and RAE of 64.14% and 27.73% for the German and Australian data sets respectively.