Miracle-dz/UniFAR-FTUs huggingface dataset:
Text to Image Synthesis for Improved Image Captioning

Generating textual descriptions of images has been an important topic in computer vision and natural language processing. A number of techniques based on deep learning have been proposed on this topic. These techniques use human-annotated images for training and testing the models. These models require a large number of training data to perform at their full potential. Collecting human generated images with associative captions is expensive and time-consuming. In this paper, we propose an image captioning method that uses both real and synthetic data for training and testing the model. We use a Generative Adversarial Network (GAN) based text to image generator to generate synthetic images. We use an attention-based image captioning method trained on both real and synthetic images to generate the captions. We demonstrate the results of our models using both qualitative and quantitative analysis on popularly used evaluation metrics. We show that our experimental results achieve two fold benefits of our proposed work: i) it demonstrates the effectiveness of image captioning for synthetic images, and ii) it further improves the quality of the generated captions for real images, understandably because we use additional images for training.
Makeup Style Transfer on Low-quality Images with Weighted Multi-scale Attention

Facial makeup style transfer is an extremely challenging sub-field of image-to-image-translation. Due to this difficulty, state-of-the-art results are mostly reliant on the Face Parsing Algorithm, which segments a face into parts in order to easily extract makeup features. However, this algorithm can only work well on high-definition images where facial features can be accurately extracted. Faces in many real-world photos, such as those including a large background or multiple people, are typically of low-resolution, which considerably hinders state-of-the-art algorithms. In this paper, we propose an end-to-end holistic approach to effectively transfer makeup styles between two low-resolution images. The idea is built upon a novel weighted multi-scale spatial attention module, which identifies salient pixel regions on low-resolution images in multiple scales, and uses channel attention to determine the most effective attention map. This design provides two benefits: low-resolution images are usually blurry to different extents, so a multi-scale architecture can select the most effective convolution kernel size to implement spatial attention; makeup is applied on both a macro-level (foundation, fake tan) and a micro-level (eyeliner, lipstick) so different scales can excel in extracting different makeup features. We develop an Augmented CycleGAN network that embeds our attention modules at selected layers to most effectively transfer makeup. Our system is tested with the FBD data set, which consists of many low-resolution facial images, and demonstrate that it outperforms state-of-the-art methods, particularly in transferring makeup for blurry images and partially occluded images.
Learning to Implicitly Represent 3D Human Body From Multi-scale Features and Multi-view Images

Reconstruction of 3D human bodies, from images, faces many challenges, due to it generally being an ill-posed problem. In this paper we present a method to reconstruct 3D human bodies from multi-view images, through learning an implicit function to represent 3D shape, based on multi-scale features extracted by multi-stage end-to-end neural networks. Our model consists of several end-to-end hourglass networks for extracting multi-scale features from multi-view images, and a fully connected network for implicit function classification from these features. Given a 3D point, it is projected to multi-view images and these images are fed into our model to extract multiscale features. The scales of features extracted by the hourglass networks decrease with the depth of our model, which represents the information from local to global scale. Then, the multi-scale features as well as the depth of the 3D point are combined to a new feature vector and the fully connected network classifies the feature vector, in order to predict if the point lies inside or outside of the 3D mesh. The advantage of our method is that we use both local and global features in the fully connected network and represent the 3D mesh by an implicit function, which is more memory-efficient. Experiments on public datasets demonstrate that our method surpasses previous approaches in terms of the accuracy of 3D reconstruction of human bodies from images.
Computational Data Analysis for First Quantization Estimation on JPEG Double Compressed Images

Multimedia Forensics experts work consists in providing answers about integrity of a specific media content and from where it comes from. Exploitation of any traces from JPEG double compressed images is often one of the main investigative path to be used for these purposes. Thus it is fundamental to have tools and algorithms able to safely estimate the first quantization matrix to further proceed with camera model identification and related tasks. In this paper, a technique based on extensive simulation is proposed, with the aim to infer the first quantization for a certain numbers of Discrete Cosine Transform (DCT) coefficients exploiting local image statistics without using any a-priori knowledge. The method provides also a reliable confidence value for the estimation which is of great importance for forensic purposes. Experimental results w.r. t. the state-of-the-art demonstrate the effectiveness of the proposed technique both in terms of precision and overall reliability.
Robust Localization of Retinal Lesions via Weakly-supervised Learning

Retinal fundus images reveal the condition of retina, blood vessels and optic nerve, and is becoming widely adopted in clinical work because any subtle changes to the structures at the back of the eyes can affect the eyes and indicate the overall health. Recently, machine learning, in particular deep learning by convolutional neural network (CNN), has been increasingly adopted for computer-aided detection (CAD) of retinal lesions. However, a significant barrier to the high performance of CNN based CAD approach is the lack of sufficient labeled image samples for training. Unlike the fully-supervised learning which relies on pixel-level annotation of pathology in fundus images, this paper presents a new approach to discriminate the location of various lesions based on image-level labels via weakly learning. More specifically, our proposed method leverages the multilevel feature maps and classification score to cope with both bright and red lesions in fundus images. To enhance capability of learning less discriminative parts of objects (e.g. small blobs of microaneurysms opposed to bulk of exudates), the classifier is regularized by refining images with corresponding labels. The experimental results of the performance evaluation and benchmarking at both image-level and pixel-level on the public DIARETDB1 dataset demonstrate the feasibility and excellent potentials of our method in practical usage.
Challenges And Prospects For Quality Higher Education Via E-Learning Platforms In Private Universities: A Case Study Of Acacia University Zambia

This study was purposed to explore the challenges and prospects for quality higher education through effective e learning management in Zambian Private Universities with special reference to Acacia University Zambia (AUZ) (pseudo). In order to gain in-depth understanding of the phenomena, the study adopted a case study method which incorporated a qualitative paradigm. A purposive sampling criteria was adopted in which persons in line faculty portfolios (deans, HoDs, directors) were subjected to an in-depth structured interview to probe the issues incidental to the delivery of education services through e learning platforms as occasioned by the tragic advent of the COVID-19 pandemic. The students from the medical faculty were equally subjected to a semi-structured interview as to their experiential perspective on the available institutional e learning platforms. The preliminary results of the study indicate that from the notification of closure of universities by governmental authorities which took effect on 20 th March 2020 in response to the COVID-19 pandemic outbreak, the institution integrated / blended both the full time and distance learning students on ClanED platform which was mostly through textual interaction and posting of teaching/learning materials. In trying to enhance efficiency and effective delivery of quality education service, the institution later incorporated Zoom and further brought on board google meet as virtual learning platforms. It is, however, pertinent to note that both the ClanED and Zoom platforms had challenges, including but not limited to; lack of effective interactivism for ClanED and number limits / network intermittence for Zoom respectively. The later was also the same problem bedeviling the google meet but specifically on network connectivity from both institutional and learner ends. Overall, field evidence both from faculty line managers and students suggest management and co-ordination inadequacies between administrators of the platforms and the faculty line managers, which greatly affected effective delivery of services. Nevertheless, it was gratifying to note that faculty line managers, directors and platform administrators had instituted various measures aimed at rectifying the operational and management hitches and glitches encountered in the provision education service via the e learning platforms through adoption of the google meet platform as the platform of choice, and capacity building among all line managers, administrators and student orientations.
CKG: Dynamic Representation Based on Context and Knowledge Graph

Recently, neural language representation models pre-trained on large corpus can capture rich co-occurrence information and be fine-tuned in downstream tasks to improve the performance. As a result, they have achieved state-of-the-art results in a large range of language tasks. However, there exists other valuable semantic information such as similar, opposite, or other possible meanings in external knowledge graphs (KGs). We argue that entities in KGs could be used to enhance the correct semantic meaning of language sentences. In this paper, we propose a new method CKG: Dynamic Representation Based on Context and Knowledge Graph. On the one side, CKG can extract rich semantic information of large corpus. On the other side, it can make full use of inside information such as co-occurrence in large corpus and outside information such as similar entities in KGs. We conduct extensive experiments on a wide range of tasks, including QQP, MRPC, SST-5, SQuAD, CoNLL 2003, and SNLI. The experiment results show that CKG achieves SOTA 89.2 on SQuAD compared with SAN (84.4), ELMo (85.8), and $\text{BERT}_{Base}$ (88.5).
Segmentation of Intracranial Aneurysm Remnant in MRA using Dual-Attention Atrous Net

Due to the advancement of non-invasive medical imaging modalities like Magnetic Resonance Angiography (MRA), an increasing number of Intracranial Aneurysm (IA) cases are being reported in recent years. The IAs are typically treated by so-called endovascular coiling, where blood flow in the IA is prevented by embolization with a platinum coil. Accurate quantification of the IA Remnant (IAR), i.e. the volume with blood flow present post treatment is the utmost important factor in choosing the right treatment planning. This is typically done by manually segmenting the aneurysm remnant from the MRA volume. Since manual segmentation of volumetric images is a labour-intensive and error-prone process, development of an automatic volumetric segmentation method is required. Segmentation of small structures such as IA, that may largely vary in size, shape, and location is considered extremely difficult. Similar intensity distribution of IAs and surrounding blood vessels makes it more challenging and susceptible to false positive. In this paper we propose a novel 3D CNN architecture called Dual-Attention Atrous Net (DAtt-ANet), which can efficiently segment IAR volumes from MRA images by reconciling features at different scales using the proposed Parallel Atrous Unit (PAU) along with the use of self-attention mechanism for extracting fine-grained features and intra-class correlation. The proposed DAtt- ANet model is trained and evaluated on a clinical MRA image dataset of IAR consisting of 46 subjects. We compared the proposed DAtt-ANet with five state-of-the-art CNN models based on their segmentation performance. The proposed DAtt-ANet outperformed all other methods and was able to achieve a five-fold cross-validation DICE score of 0.73 ± 0.06.
Design of Sparse Multiband Signal for Precise Positioning With Joint Low-Complexity Time Delay and Carrier Phase Estimation

This paper presents a methodology to design a sparse multiband ranging signal with a large virtual bandwidth, from which time delay and carrier phase are estimated by a low complexity multivariate maximum likelihood (ML) method. In the estimation model for a multipath channel, not all reflected paths are considered, and time delay and carrier phase are estimated in a step-wise manner to further reduce the computational load. By introducing a measure of dependence and a measure of bias for a multipath reflection, we analyse the bias, precision and accuracy of time delay and carrier phase estimation. Since these two indicators are determined by the signal spectrum pattern, they are used to formulate an optimization for signal design. By solving the optimization problem, only a few bands from the available signal spectrum are selected for ranging. Consequently, the designed signal only occupies a small amount of signal spectrum but has a large virtual bandwidth and can thereby still offer a high ranging precision with only a small bias, based on the low-complexity simplified ML method. Numerical and laboratory experiments are carried out to evaluate the ranging performance of the proposed estimation method based on sparsely selected signal bands. Relative positioning, in which we only measure a change in position, based on either the time delay estimates or the carrier phase estimates, is presented as a proof-of-concept for precise positioning. The results show that positioning based on only 7 out of 16 signal bands, sparsely placed in the available spectrum, achieves a decimeter level accuracy when using time delay estimates, and a millimeter level accuracy when using carrier phase estimates. Compared with the case of using all available bands, and without largely decreasing the positioning performance, the computational complexity when using the sparse multiband signal can be reduced by about 80%.
Object-oriented Map Exploration and Construction Based on Auxiliary Task Aided DRL

Environment exploration by autonomous robots through deep reinforcement learning (DRL) based methods has attracted more and more attention. However, existing methods usually focus on robot navigation to single or multiple fixed goals, while ignoring the perception and construction of external environments. In this paper, we propose a novel environment exploration task based on DRL, which requires a robot fast and completely perceives all objects of interest, and reconstructs their poses in a global environment map, as much as the robot can do. To this end, we design an auxiliary task aided DRL model, which is integrated with the auxiliary object detection and 6-DoF pose estimation components. The outcome of auxiliary tasks can improve the learning speed and robustness of DRL, as well as the accuracy of object pose estimation. Comprehensive experimental results on the indoor simulation platform AI2-THOR have shown the effectiveness and robustness of our method.
Isolation Forest-Based Mechanism to Defend against Interest Flooding Attacks in Named Data Networking

Interest flooding attacks (IFAs) are widely regarded as being among the most harmful security risks in named data networking (NDN). Through an IFA, the attacker injects numerous Interest packets into a network to drain network resources such as bandwidth, caching capacity, and computational capacity, which can seriously affect the normal data content requests of legitimate consumers and degrade the network quality of service (QoS). To design a high-efficiency IFA mitigation scheme, it is critical to detect attacks accurately and rapidly. Therefore, there is high interest in developing an optimized attack detection scheme. In this study, the concept of an isolation forest (iForest) is introduced to develop an IFA detection mechanism in which the iForest construction process isolates abnormal and legitimate prefixes. This approach enables malicious prefixes to be identified among abnormal prefixes to mitigate IFAs by restricting the forwarding of malicious Interest packets. The results of extensive simulations show that the proposed iForest-based IFA detection mechanism (IFDM) outperforms other related schemes in terms of attack detection accuracy and speed and thus can offer effective support for preserving NDN QoS.
On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID

The past years have witnessed great progress on remote sensing (RS) image interpretation and its wide applications. With RS images becoming more accessible than ever before, there is an increasing demand for the automatic interpretation of these images. In this context, the benchmark datasets serve as an essential prerequisites for developing and testing intelligent interpretation algorithms. After reviewing existing benchmark datasets in the research community of RS image interpretation, this article discusses the problem of how to efficiently prepare a suitable benchmark dataset for RS image interpretation. Specifically, we first analyze the current challenges of developing intelligent algorithms for RS image interpretation with bibliometric investigations. We then present the general guidances on creating benchmark datasets in efficient manners. Following the presented guidances, we also provide an example on building RS image dataset, i.e., Million Aerial Image Dataset (Online. Available: https://captain-whu.github.io/DiRS/0), a new large-scale benchmark dataset containing a million instances for RS image scene classification. Several challenges and perspectives in RS image annotation are finally discussed to facilitate the research in benchmark dataset construction. We do hope this article will provide the RS community an overall perspective on constructing large-scale and practical image datasets for further research, especially data-driven ones.
NephCNN: A deep-learning framework for vessel segmentation in nephrectomy laparoscopic videos

Objective: In the last years, Robot-assisted partial nephrectomy (RAPN) is establishing as elected treatment for renal cell carcinoma (RCC). Reduced field of view, field occlusions by surgical tools, and reduced maneuverability may potentially cause accidents, such as unwanted vessel resection with consequent bleeding. Surgical Data Science (SDS) can provide effective context-aware tools for supporting surgeons. However, currently no tools have been exploited for automatic vessels segmentation from nephrectomy laparoscopic videos. Herein, we propose a new approach based on adversarial Fully Convolutional Neural Networks (FCNNs) to kidney vessel segmentation from nephrectomy laparoscopic vision. Methods: The proposed approach enhances existing segmentation framework by (i) encoding 3D kernels for spatio-temporal features extraction to enforce pixel connectivity in time, and (ii) perform training in adversarial fashion, which constrains vessels shape. Results: We performed a preliminary study using 8 different RAPN videos (1871 frames), the first in the field, achieving a median Dice Similarity Coefficient of 71.76%. Conclusions: Results showed that the proposed approach could be a valuable solution with a view to assist surgeon during RAPN.
Cooperative Non-Orthogonal Multiple Access for Beyond 5G Networks

In the beyond 5G networks, extraordinary demands for data rates and capacity are to be met. A possible candidate to address these challenges is Non-Orthogonal Multiple Access (NOMA) technique, which leads to higher diversity gains and massive connectivity. One caveat of NOMA is the increased receiver complexity to nullify the Inter User Interference (IUI) through Successive Interference Cancellation (SIC). In this paper, a cooperative relaying scheme is employed to improve the overall diversity gain and data rates of the NOMA system. Extrinsic Information Transfer (EXIT) charts are employed to examine the cooperative NOMA system’s user fairness as well as its performance while implementing the IRregular Convolutional Code (IRCC). The EXIT chart using IRCC evaluates the convergence analysis for the proposed system. Furthermore, the implementation of EXIT charts for optimization convergence is exploited by using power optimization and the SIC for the joint rate to evaluate the fairness of the system. Simulation results shows that cooperative NOMA helps to achieve higher diversity gain and improved data rate for the cooperative NOMA system.
Late Fusion of Bayesian and Convolutional Models for Action Recognition

The activities we do in our daily-life are generally carried out as a succession of atomic actions, following a logical order. During a video sequence, actions usually follow a logical order. In this paper, we propose a hybrid approach resulting from the fusion of a deep learning neural network with a Bayesian-based approach. The latter models human-object interactions and transition between actions. The key idea is to combine both approaches in the final prediction. We validate our strategy in two public datasets: CAD-120 and Watch-n-Patch. We show that our fusion approach yields performance gains in accuracy of respectively +4% and +6% over a baseline approach. Temporal action recognition performances are clearly improved by the fusion, especially when classes are imbalanced.
Classification of spatially enriched pixel time series with convolutional neural networks

Satellite Image Time Series (SITS), MRI sequences, and more generally image time series, constitute $2D+t$ data providing spatial and temporal information about an observed scene. Given a pattern recognition task such as image classification, considering jointly such rich information is crucial during the decision process. Nevertheless, due to the complex representation of the data-cube, spatio-temporal features extraction from $2D+t$ data remains difficult to handle. We present in this article an approach to learn such features from this data, and then to proceed to their classification. Our strategy consists in enriching pixel time series with spatial information. It is based on Random Walk to build a novel segment-based representation of the data, passing from a $2D+t$ dimension to a $2D$ one, without loosing too much spatial information. Such new representation is then involved in an end-to-end learning process with a classical 2D Convolutional Neural Network (CNN) in order to learn spatiotemporal features for the classification of image time series. Our approach is evaluated on a remote sensing application for the mapping of agricultural crops. Thanks to a visual attention mechanism, the proposed $2D$ spatio-temporal representation makes also easier the interpretation of a SITS to understand spatiotemporal phenomenons related to soil management practices.