Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Monitoring and Evaluation on Implementation of Infrastructural Projects Funded by Vihiga County Government, Kenya

Monitoring and Evaluation is an integral part of project performance. Devolution of development agenda in Kenya has faced challenges and tests of accountability. Efficiency and performance of county governments are under scrutiny which has necessitated need for counties to adopt and intensify M&E systems in their project cycles. However, cases of project failures increasingly witnessed across counties rise concerns to whether such occurrences are attributable to low monitoring and evaluation systems. A study was carried out on infrastructural projects funded by the county government of Vihiga, Kenya to establish effect of monitoring and evaluation on implementation of these projects. The study established positive relationships between study variables and M&E performance. Stakeholder engagement, budgetary allocation, technical capacity and technology were found to have significant effects on the performance of M&E systems of Vihiga county, the variables contributed to an aggregate of 74.5% variation change in M&E performance. Regression statistics revealed that by holding all other independent variables at constant zero, a unit increase in stakeholder engagement, budgetary allocation, technical capacity and technology would lead to a corresponding increase in M&E performance by 0.482, 0.422, 0.415, 0.407 respectively. The findings also revealed insufficient M&E budgetary provisions, imprudent use of M&E funds outside planned schedules, low levels of stakeholder engagement, inadequate feedback absorption particularly in decision making, low staff motivation and technological literacy depicted by mean averages 2.16,1.16,2.12,1.99 and 2.03 respectively. These contributed to low M&E systems consequently impacting on the overall efficiency of project implementation. Unique Contribution to Theory, Practice and Policy: The study recommends strengthening of Public Finance Management Regulations and Guidelines of 2015 (PFM 2015) and other accountability frameworks to ensure prudent use of M&E funds. The study iterates the critical role of stakeholders in enhancing prosperity of M&E systems and the need to strengthen and increase participation at all critical levels of decisions making. Project institutions to investments substantially in technological literacy to enhance usability and experience in M&E technology approaches.
Financing Diversification and Profitability of Islamic Banking in Indonesia

This study aims to determine the effect of Islamic bank financing diversification based on economic sectors and based on the type of use on profitability. This study uses financing data for Islamic Banks and Islamic Business Units in 2014-2018. The data in this study are the times series data. Data analyzed by used multiple linear regression analyses. This research provides that results that the HHI of diversification of Islamic bank financing based on the economic sector is positively related to increasing the profitability of Islamic banks. It means diversification of financing based on high economic sectors, has an impact on reducing the profitability of Islamic banks in Indonesia. While the HHI of diversification of financing by type of use give a significant influence in increasing the profitability of Islamic banks in a negative direction. If Islamic banking only focuses on channeling financing to the consumption sector, it can have an impact on decreasing profitability. So that Islamic banking needs to equalize the distribution of consumption to the working capital and investment sectors. The results of this study are interesting because it can be used as a reference for Islamic banking in determining the policy of diversification of financing to obtain high profitability with low risk.
Collaborative Human Machine Attention Module for Character Recognition

The deep learning models, which include attention mechanisms, are shown to enhance the performance and efficiency of the various computer vision tasks such as pattern recognition, object detection, face recognition, etc. Although the visual attention mechanism is the source of inspiration for these models, recent attention models consider ’attention’ as a pure machine vision optimization problem, and visual attention remains the most neglected aspect. Therefore, this paper presents a collaborative human and machine attention module which considers both visual and network’s attention. The proposed module is inspired by the dorsal (‘where’) pathways of visual processing and can be integrated with any convolutional neural network (CNN) model. First, the module computes the spatial attention map from the input feature maps, which is then combined with the visual attention maps. The visual attention maps are created using eye-fixations obtained by performing an eye-tracking experiment with human participants. The visual attention map covers the highly salient and discriminating image regions as humans tend to focus on such regions, whereas the other relevant image regions are processed by spatial attention map. The combination of these two maps results in the finer refinement in feature maps, resulting in improved performance. The comparative analysis reveals that our model not only shows significant improvement over the baseline model but also outperforms the other models. We hope that our findings using a collaborative human-machine attention module will be helpful in other computer vision tasks as well.
SNR Coverage Probability Analysis of RIS-Aided Communication Systems

The reconfigurable intelligent surface (RIS) technique has received many interests, thanks to its advantages of low cost, easy deployment, and high controllability. It is acknowledged that the RIS can significantly improve the quality of signal transmission, especially in the line-of-sight-blocked scenarios. Therefore, it is critical to analyze the corresponding signal-to-noise ratio (SNR) coverage probability of RIS-aided communication systems. In this correspondence, we consider many practical issues to analyze the SNR coverage probability. We employ the realistic path loss model and Rayleigh fading model as large-scale and small-scale channel models, respectively. Meanwhile, we take the number and size of the RIS elements, as well as the placement of the RIS plane into considerations. We first derive the exact closed-form SNR coverage probability for a single element. Afterward, with the moment matching method, a highly accurate approximation of SNR coverage probability is formulated as the ratio of the upper incomplete Gamma function and Gamma function, allowing an arbitrary number of elements in the RIS. Finally, we comprehensively evaluate the impacts of essential factors on the SNR coverage probability, such as the number and size of the element, the coefficients of fading channel, and the angles of incidence and RIS plane. Overall, this work provides a succinct and general expression of SNR coverage probability, which can be helpful in the performance evaluation and practical implementation of the RIS.
Self-Tuning Neural Network PID With Dynamic Response Control

PID controllers are widely used and adaptable to various types of systems. However, for the response to be adequate under different conditions, the PID gains must be adjusted. The tuning is made according to the difference between the reference value and the real value (error). This work presents a self-adjusting PID controller based on a backpropagation artificial neural network. The network calculates the appropriate gains according to the desired output, that is, the dynamic response desired which is composed of the transient part and the stationary part of the step response of a system. The contribution of the work is that in addition to using the error for network training, the maximum desired values of overshoots, settling times, and stationary errors were used as input data for the network. An offline training database was created using genetic algorithms to obtain the dynamic response data associated with PID gains. The genetic algorithm allows getting data in different operating ranges and allows using only stable gains combinations. The database was used for training. Subsequently, the neural network estimates an appropriate gain combination, adapting to the error and the desired response. The method performance is evaluated by controlling the speed of a direct current motor. The results indicate an average error of 4% for the database between the requested and system response. On the other hand, the gains estimated by the network in the test dataset (1544 combinations) did not cause instability and complying with the expected dynamic response in 86% of the dataset.
A 20–36-GHz Voltage-Controlled Analog Distributed Attenuator With a Wide Attenuation Range and Low Phase Imbalance

A K-/Ka-band distributed attenuator in a 28-nm CMOS process is presented for a phased array. The proposed voltage-controlled distributed attenuator is designed using nonuniform varistors for a wide attenuation range with a compact size. An attenuation range of 25 dB was achieved with only three sections, which are 90° apart. The phase imbalance of the proposed attenuator is minimized using an artificial quarter-wave transmission line with series-connected varistors. The measured insertion loss of the proposed attenuator is 1.6–2.5 dB from 20 to 36 GHz. The insertion phase imbalance is less than 10° within the same bandwidth. The input 1-dB compression point is 10.6 dBm (defined as the 1-dB drop in the maximum attenuation range) at 28 GHz. The total chip size, including pads, is 0.77 ${\times }$ 0.42 mm2. The proposed attenuator has the highest attenuation range relative to the chip area compared with previously distributed attenuators.
S2I-Bird: Sound-to-Image Generation of Bird Species using Generative Adversarial Networks

Generating images from sound is a challenging task. This paper proposes a novel deep learning model that generates bird images from their corresponding sound information. Our proposed model includes a sound encoder in order to extract suitable feature representations from audio recordings, and then it generates bird images that corresponds to its calls using conditional generative adversarial networks (cGANs) with auxiliary classifiers. We demonstrate that our model produces better image generation results which outperforms other state-of-the-art methods in a similar context.
A Comparison of Neural Network Approaches for Melanoma Classification

Melanoma is the deadliest form of skin cancer and it is diagnosed mainly visually, starting from initial clinical screening and followed by dermoscopic analysis, biopsy and histopathological examination. A dermatologist's recognition of melanoma may be subject to errors and may take some time to diagnose it. In this regard, deep learning can be useful in the study and classification of skin cancer. In particular, by classifying images with Deep Neural Network methodologies, it is possible to obtain comparable or even superior results compared to those of dermatologists. In this paper, we propose a methodology for the classification of melanoma by adopting different deep learning techniques applied to a common dataset, composed of images from the ISIC dataset and consisting of different types of skin diseases, including melanoma on which we applied a specific pre-processing phase. In particular, a comparison of the results is performed in order to select the best effective neural network to be applied to the problem of recognition and classification of melanoma. Moreover, we also evaluate the impact of the preprocessing phase on the final classification. Different metrics such as accuracy, sensitivity, and specificity have been selected to assess the goodness of the adopted neural networks and compare them also with the manual classification of dermatologists.
Thermal Characterisation of Unweighted and Weighted Networks

Thermodynamic characterisations or analogies have proved to provide powerful tools for the statistical analysis of network populations or time series, together with the identification of structural anomalies that occur within them. For instance, classical Boltzmann statistics together with the corresponding partition function have been used to apply the tools of statistical physics to the analysis of variations in network structure. However, the physical analogy adopted in this analysis, together with the interpretation of the resulting system of particles is sometimes vague and remains an open question. This, in turn, has implications concerning the definition of quantities such as temperature and energy. In this paper, we take a novel view of the thermal characterisation where we regard the edges in a network as the particles of the thermal system. By considering networks with a fixed number of nodes we obtain a conservation law which applies to the particle occupation configuration. Using this interpretation, we provide a physical meaning for the temperature which is related to the number of network nodes and edges. This provides a fundamental description of a network as a thermal system. If we further interpret the elements of the adjacency matrix as the binary microstates associated with edges, this allows us to further extend the analysis to systems with edge-weights. We thus introduce the concept of the canonical ensemble into the thermal network description and the corresponding partition function and then use this to compute the thermodynamic quantities. Finally, we provide numerical experiments on synthetic and real-world data-sets to evaluate the thermal characterisations for both unweighted and weighted networks.
Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Their mobility and their ability to establish line of sight (LOS) links with the users made them key solutions for many potential applications. In the same vein, artificial intelligence (AI) is growing rapidly nowadays and has been very successful, particularly due to the massive amount of the available data. As a result, a significant part of the research community has started to integrate intelligence at the core of UAVs networks by applying AI algorithms in solving several problems in relation to drones. In this article, we provide a comprehensive overview of some potential applications of AI in UAV-based networks. We also highlight the limits of the existing works and outline some potential future applications of AI for UAVs networks.
Learning Interpretable Representation for 3D Point Clouds

Point clouds have emerged as a popular representation of 3D visual data. With a set of unordered 3D points, one typically needs to transform them into latent representation before further classification and segmentation tasks. However, one cannot easily interpret such encoded latent representation. To address this issue, we propose a unique deep learning framework for disentangling body-type and pose information from 3D point clouds. Extending from autoencoder, we advance adversarial learning a selected feature type, while classification and data recovery can be additionally observed. Our experiments confirm that our model can be successfully applied to perform a wide range of 3D applications like shape synthesis, action translation, shape/action interpolation, and synchronization.
Thermal Image Enhancement using Generative Adversarial Network for Pedestrian Detection

Infrared imaging has recently played an important role in a wide range of applications including video surveillance, robotics and night vision. However, infrared cameras often suffer from some limitations, essentially about low-contrast and blurred details. These problems contribute to the loss of observation of target objects in infrared images, which could limit the feasibility of different infrared imaging applications. In this paper, we mainly focus on the problem of pedestrian detection on thermal images. Particularly, we emphasis the need for enhancing the visual quality of images before performing the detection step. To address that, we propose a novel thermal enhancement architecture called TE-GAN based on Generative Adversarial Network, and composed of two modules contrast enhancement and denoising with a post-processing step for edge restoration in order to improve the overall image quality. The effectiveness of the proposed architecture is assessed by means of visual quality metrics and better results are obtained compared to the original thermal images and to the obtained results by other existing enhancement methods. These results have been conducted on a subset of KAIST dataset that we make available to encourage research in this direction11https://github.com/AmineMarnissi/TE-GAN, Using the same dataset, the impact of the proposed enhancement architecture has been demonstrated on the detection results by obtaining better performance with a significant margin using YOLOv3 detector.
Aerial Road Segmentation in the Presence of Topological Label Noise

The availability of large-scale annotated datasets has enabled Fully-Convolutional Neural Networks to reach outstanding performance on road extraction in aerial images. However, high-quality pixel-level annotation is expensive to produce and even manually labeled data often contains topological errors. Trading off quality for quantity, many datasets rely on already available yet noisy labels, for example from OpenStreetMap. In this paper, we explore the training of custom U-Nets built with ResNet and DenseNet backbones using noise-aware losses that are robust towards label omission and registration noise. We perform an extensive evaluation of standard and noise-aware losses, including a novel Bootstrapped DICE-Coefficient loss, on two challenging road segmentation benchmarks. Our losses yield a consistent improvement in overall extraction quality and exhibit a strong capacity to cope with severe label noise. Our method generalizes well to two other fine-grained topology delineation tasks: surface crack detection for quality inspection and cell membrane extraction in electron microscopy imagery.
A deep learning approach for the segmentation of myocardial diseases

Cardiac left ventricular (LV) segmentation is a paramount essential step for both diagnosis and treatment of cardiac pathologies such as ischemia, myocardial infarction, arrhythmia and myocarditis. However, this segmentation is challenging due to high variability across patients and the potential lack of contrast between structures. In this work, we propose and evaluate a (2.5D) SegU-Net model based on the fusion of two deep learning segmentation techniques (U-Net and Seg-Net) for automated LGE-MRI (Late gadolinium enhanced magnetic resonance imaging) myocardial disease (infarct core and no-reflow region) quantification in a new multifield expert annotated dataset. Given that the scar tissue represents a small part of the whole MRI slices, we focused on myocardium area. Segmentation results show that this preprocessing step facilitate the learning procedure. In order to solve the class imbalance problem, we propose to apply the Jaccard loss and the Focal Loss as optimization loss function and to integrate a class weights strategy into the objective function. Late combination has been used to merge the output of the best trained models on a different set of hyperparameters. The final network segmentation performances will be useful for future comparison of new methods to the current related work for this task. A total number of 2237 of slices (320 cases) were used for training/validation and 210 slices (35 cases) were used for testing. Experiments on our proposed dataset, using several evaluation metrics such Jaccard distance (IOU), Accuracy and Dice similarity coefficient (DSC), demonstrate efficiency performance in quantifying different zones of myocardium infarction across various patients. As compared to the second intra-observer study, our testing results showed that the SegU-Net prediction model leads to these average Dice coefficients over all segmented tissue classes, respectively: ‘Back-ground’: 0.99999, ‘Myocardium’: 0.99434, ‘Infarctus’: 0.95587, ‘Noreflow’: 0.78187 outperforming seven previously proposed methods.
Multi-view Object Detection Using Epipolar Constraints within Cluttered X-ray Security Imagery

Automatic detection for threat object items is an increasing emerging area of future application in X-ray security imagery. Although modern X-ray security scanners can provide two or more views, the integration of such object detectors across the views has not been widely explored with rigour. Therefore, we investigate the application of geometric constraints using the epipolar nature of multi-view imagery to improve object detection performance. Furthermore, we assume that images come from uncalibrated views, such that a method to estimate the fundamental matrix using ground truth bounding box centroids from multiple view object labels is proposed. In addition, detections are given a confidence probability based on its similarity with respect to the distribution of the distance to the epipolar line. This probability is used as confidence weights for merging duplicated predictions using non-maximum suppression. Using a standard object detector (YOLOv3), our technique increases the average precision of detection by 2.8% on a dataset composed of firearms, laptops, knives and cameras. These results indicate that the integration of images at different views significantly improves the detection performance of threat items of cluttered X-ray security images.
Efficient-Receptive Field Block with Group Spatial Attention Mechanism for Object Detection

Object detection has been paid rising attention in computer vision field. Convolutional Neural Networks (CNNs) extract high-level semantic features of images, which directly determine the performance of object detection. As a common solution, embedding integration modules into CNNs can enrich extracted features and thereby improve the performance. However, the instability and inconsistency of internal multiple branches exist in these modules. To address this problem, we propose a novel multibranch module called Efficient-Receptive Field Block (E-RFB), in which multiple levels of features are combined for network optimization. Specifically, by downsampling and increasing depth, the E-RFB provides sufficient RF. Second, in order to eliminate the inconsistency across different branches, a novel spatial attention mechanism, namely, Group Spatial Attention Module (GSAM) is proposed. The GSAM gradually narrows a feature map by channel grouping; thus it encodes the information between spatial and channel dimensions into the final attention heat map. Third, the proposed module can be easily joined in various CNNs to enhance feature representation as a plug-and-play component. With SSD-style detectors, our method halves the parameters of the original detection head and achieves high accuracy on the PASCAL VOC and MS COCO datasets. Moreover, the proposed method achieves superior performance compared with state-of-the-art methods based on similar framework.
Switching Dynamical Systems with Deep Neural Networks

The problem of uncovering different dynamical regimes is of pivotal importance in time series analysis. Switching dynamical systems provide a solution for modeling physical phenomena whose time series data exhibit different dynamical modes. In this work we propose a novel variational RNN model for switching dynamics allowing for both non-Markovian and nonlinear dynamical behavior between and within dynamic modes. Attention mechanisms are provided to inform the switching distribution. We evaluate our model on synthetic and empirical datasets of diverse nature and successfully uncover different dynamical regimes and predict the switching dynamics.