Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Radio Resource Allocation and SIC Ordering in NOMA-Based Networks Using Submodularity and Matching Theory

In this paper, we propose a novel joint radio resource allocation and successive interference cancellation (SIC) ordering algorithm for the downlink scenario of a power-domain sparse code multiple access (PSMA) based wireless network. To this end, we formulate a novel joint radio and SIC ordering optimization problem. Our aim is to maximize sum rate over codebook assignment, transmit power, and SIC ordering variables. Since the proposed optimization problem is NP-hard and mathematically intractable, we exploit alternate search method (ASM). Based on this algorithm, the proposed optimization problem is decomposed into three sub-problems, namely, codebook assignment, power allocation, and SIC ordering. To solve the codebook assignment sub-problem, we exploit matching theory and submodularity and compared them from the performance and complexity perspectives. Due to the fact that PD-NOMA-based networks adopt SIC at the receiver side, the proposed SIC ordering algorithm is also studied for this type of networks. Moreover, the proposed algorithms are investigated from the performance, convergence, and computational complexity perspectives. Numerical results show that the proposed SIC ordering method outperforms the conventional one by approximately 48% in the studied NOMA-based networks.
A deep separable neural network for human tissue identification in three-dimensional optical coherence tomography images

Abstract This research proposes a dilated depthwise separable network for human tissue identification using three-dimensional (3D) optical coherence tomography (OCT) images. Automatic human tissue identification has made it possible for fast pathological tissue analyses, detecting tissue changes over time, and efficiently making a precise therapy treatment plan. 3D medical image classification is a challenging task because of the indistinct tissue characteristics and computational efficiency. To address the challenge, a deep dilated depthwise separable convolutional neural network is proposed in this research. A depthwise separable architecture is introduced to improve parameter utilization efficiency. Dilated convolutions are applied to systematically aggregate multiscale contextual information and provide a large receptive field with a small number of trainable weights, which provide computational benefit. 2D convolutions are applied in the proposed model to enhance the computational efficiency. The constructed model is tested by performing a multi-class human thyroid tissue classification on 3D OCT images. For comparison, experimental results are obtained for texture feature-based shallow learning models and typical deep learning classification models. The results show that the proposed DDSCN model outperforms those state-of-art models and can improve accuracy by 3.2% compared with the best texture-based model and 2.27% compared with the best CNN model. The proposed deep model provides significant potential for the applicability of the deep learning technique to analyze medical images of human tissue while advancing the next generation of OCT-based real-time surgery image guidance.
Target Tracking and Classification Using Compressive Measurements of MWIR and LWIR Coded Aperture Cameras

Pixel-wise Code Exposure (PCE) camera is one type of compressive sensing camera that has low power consumption and high compression ratio. Moreover, a PCE camera can control individual pixel exposure time that can enable high dynamic range. Conventional approaches of using PCE camera involve a time consuming and lossy process to reconstruct the original frames and then use those frames for target tracking and classification. In this paper, we present a deep learning approach that directly performs target tracking and classification in the compressive measurement domain without any frame reconstruction. Our approach has two parts: tracking and classification. The tracking has been done using YOLO (You Only Look Once) and the classification is achieved using Residual Network (ResNet). Extensive experiments using mid-wave infrared (MWIR) and long-wave infrared (LWIR) videos demonstrated the efficacy of our proposed approach.
Blockchain-based secure and fair crowdsourcing scheme

The crowdsourcing schemes which utilize the social network to solve complex tasks are an important part of open cooperation over the Internet. Although blockchain-based crowdsourcing schemes have considerable advantages in decentralization and data sharing, there is still a challenge to gurantee the security of crowdsourced-sensitive information and the fairness of crowdsourcing on the blockchain. To this end, this article investigates a crowdsourcing scheme based on blockchain. First, we define the basic requirements of blockchain-based crowdsourcing schemes including fairness, confidentiality, and integrity. And then, using secure hash, commitment, and homomorphic encryption, we propose a blockchain-based secure and fair crowdsourcing scheme, that is, BFC. The analysis results show that our scheme can satisfy the above requirements. Finally, the experimental results show that the computational overhead of the BFC scheme is acceptable to both the requester and the workers. In a word, our proposed crowdsourcing scheme has good expansibility in reality.
Designing Systems for Risk Based Decision Making

A common challenge security analysts face, is in making decisions on risk when facing uncertain conditions especially when risk management procedures are inapplicable. Though analysts may have experience and their intuition to depend upon, these have sometimes proven to be insufficient and it has also been identified that risk and uncertainty may be magnified by personal, system and environmental factors. Risk decision making is an integral part of security analysis, a role facilitated by system automation and design. However, designing for usable security has not sufficiently considered the implications of design to risk decision making and more so in relation to security analysts. The research aims to address this by coming up with recommendations for design, for risk based decision making.
Trayectorias de movimiento densas 3d+t como primitivas cinemáticas para el análisis de secuencias de video de profundidad

RGB-D sensors have allowed attacking many classical problems in computer vision such as segmentation, scene representations and human interaction, among many others. Regarding motion characterization, typical RGB-D strategies are limited to namely analyze global shape changes and capture scene flow fields to describe local motions in depth sequences. Nevertheless, such strategies only recover motion information among a couple of frames, limiting the analysis of coherent large displacements along time. This work presents a novel strategy to compute 3D+t dense and long motion trajectories as fundamental kinematic primitives to represent video sequences. Each motion trajectory models kinematic words primitives that together can describe complex gestures developed along videos. Such kinematic words were processed into a bag-of-kinematic-words framework to obtain an occurrence video descriptor. The novel video descriptor based on 3D+t motion trajectories achieved an average accuracy of 80% in a dataset of 5 gestures and 100 videos. Los sensores RGB-D han permitido atacar de forma novedosa muchos de los problemas clasicos en vision por computador, tales como la segmentacion, la representacion de escenas, la interaccion humano-computador, entre otros. Con respecto a la caracterizacion de movimiento, las estrategias tipicas en RGB-D estan limitadas al analisis dinamico de formas globales y a la captura de flujos de escena. Estas estrategias, sin embargo, solo recuperan informacion dinamica entre cuadros consecutivos, limitando el analisis de largos desplazamientos. Este trabajo presenta una estrategia para el calculo de trayectorias (3D+t), las cuales son fundamentales para la descripcion cinematica local, permitiendo una descripcion densa de movimiento. Cada trayectoria permite modelar palabras cinematicas, las cuales en conjunto, describen gestos complejos en los videos. Estas palabras cinematicas fueron procesadas dentro de un esquema de bolsa-de-palabras para obtener un descriptor basado ocurrencias. Este descriptor de trayectorias logro una exactitud del 80% en 5 gestos y 100 videos.
A Comprehensive Study on the Trust Management Techniques in the Internet of Things

Internet of Things (IoT) has been developed as one of the most significant technology in the future of the Internet, in which the physical objects are transformed into smart objects that can be handled and monitored via the Internet. The trust management takes a significant role in the IoT for enabling trustworthy data collection, context-awareness, and enhanced user privacy. Despite the critical significance of trust management techniques in the IoT, there is not any organized and comprehensive study in this field. Therefore, the aim of this article is to review the available methods in this field in a systematic way. In this regard, the selected techniques are categorized into four main classes, including recommendation-based, prediction-based, policy-based, and reputation-based. Then they are discussed and also compared based on some trust metrics, such as accuracy, adaptability, availability, heterogeneity, integrity, privacy, reliability, and scalability. Furthermore, some hints and challenges for further studies are outlined.
Machine Learning for Glaucoma Assessment using Fundus Images

Fundus images are widely used by ophthalmologists to assess the retina and detect glaucoma, which is, according to studies from the World Health Organization (WHO), the second cause of blindness worldwide. In this thesis, machine learning algorithms for automatic glaucoma assessment using fundus images are studied. First, two methods for automatic segmentation are proposed. The first method uses the Stochastic Watershed transformation to segment the optic cup and measures clinical features such as the Cup/Disc ratio and ISNT rule. The second method is a U-Net architecture focused on the optic disc and optic cup segmentation task. Secondly, automated glaucoma assessment systems using convolutional neural networks (CNNs) are presented. In this approach, different ImageNet-trained models are fine-tuned and used as automatic glaucoma classifiers. These new techniques allow detecting glaucoma without previous segmentation or feature extraction. Moreover, it improves the performance of other state-of-art works. Thirdly, given the difficulty of getting large amounts of glaucoma-labelled images, this thesis addresses the problem of retinal image synthesis. Two different architectures for image synthesis, the Variational Autoencoder (VAE) and Generative Adversarial Networks (GAN) architectures, were analysed. Using these models, synthetic images that were qualitative and quantitative analysed, reporting state-of-the-art performance, were generated. Finally, an adversarial model is used to create an alternative automatic glaucoma assessment system. In this part, a semi-supervised learning algorithm was implemented to reach this goal.
What Makes Action and Outcome Temporally Close to Each Other: A Systematic Review and Meta-Analysis of Temporal Binding

Temporal binding refers to the subjective compression of the temporal interval between a voluntary action and its external sensory consequences. While empirical evidence and theoretical accounts have indicated the potential linkage between temporal binding and action outcome prediction mechanisms, several questions regarding the underlying processes and the fundamental nature of temporal binding remain unanswered. Based on the sophisticated classification of predictive processes proposed by Hughes et al. (2013), we conducted a systematic, quantitative review of the binding effect as measured with two representative procedures, i.e., Libet clock procedure and interval estimation procedure. Although both procedures were designed to measure the same phenomenon, we revealed a larger effect size and higher sensitivity to perceptual moderators in binding observed with the clock procedure than with the interval estimation. Moreover, in the former, we observed different characteristics for the two perceptual shifts that comprise temporal binding. Action shifts depended more on whether one can control outcome onsets with voluntary actions. In contrast, outcome shifts depended more on the degree to which participants could predict, rather than control, the action outcome onset. These results indicate that action shift occurs based on the activation of learned action–outcome associations by planning and executing actions, while outcome shift occurs based on comparing predicted and observed outcomes. By understanding the nature of each experimental procedure and each shift, future research can use optimal methods depending on the goal. We discuss, as an example, the implications for the underlying disorders of agency in schizophrenia.
An Efficient Method for Non-Convex Blind Deconvolution

This paper considers blind deconvolution problem that to recover unknown signals <inline-formula> <tex-math notation="LaTeX">$ {f}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$ {g}$ </tex-math></inline-formula> from their convolution signal. Non-convex optimization approach is an efficient method to get the solution, but it is a challenge to find the exact solution for a non-convex optimization problem. Existing work provides a full gradient descent (GD) method converging to the global minimum from a proper initialization. However, GD algorithm is not computationally efficient. In this paper, we design the first stochastic gradient descent (SGD) algorithm that converges linearly to the exact solution. We also design a Kaczmarz algorithm which adapts the step size of SGD algorithm. It also has the linear convergence and is more computationally efficient. Finally, we analyze the global geometry of the objective function. Although the function is non-convex, its expectation has a good geometry that every local minimum is also a global optimal point. Our numerical experiments demonstrate that both SGD and Kaczmarz algorithms are more computationally efficient and can converge to the global minimum even without a proper initialization.
Multi-scale binary geometric feature description and matching for accurate registration of point clouds

Point cloud registration in military scenarios is pivotal to automatic object reconstruction and recognition. This paper proposes 1) a multi-scale binary feature representation called mLoVS (multi-scale local voxelized structure) and 2) a “min-pooling” based feature matching technique for accurate registration of tank point clouds. The key insight of our method is that traditional fixed-scale feature matching methods either suffer from limited shape information or data missing caused by occlusion, while the multi-scale way provides a flexible matching choice. In addition, the binary nature of our feature representation can alleviate the increased time budget required by multi-scale feature matching. Experiments on several sets of tank point clouds confirm the effectiveness and overall superiority of our method.
Hiding in Plain Signal: Physical Signal Overshadowing Attack on LTE

Long-Term Evolution (LTE) communication is based on an open medium; thus, a legitimate signal can potentially be counterfeited by a malicious signal. Although most LTE signaling messages are protected from modiﬁcation using cryptographic primitives, broadcast messages in LTE have never been integrity protected. In this paper, for the ﬁrst time, we present a signal injection attack that exploits the fundamental weaknesses of broadcast messages in LTE and mod-iﬁes a transmitted signal over the air. This attack, which is referred to as signal overshadowing (named SigOver ) has several advantages and differences when compared with existing attacks using a fake base station. For example, with a 3 dB power difference from a legitimate signal, the SigOver attack demonstrated a 98% success rate when compared with the 80% success rate of attacks achieved using a fake base station, even with a 35 dB power difference. Given that the SigOver attack is a novel primitive attack, it yields ﬁve new attack scenarios and implications. Finally, a discussion on two potential countermeasures leaves practical and robust defense mechanism as a future work.
Polymorphic set-theoretic types for functional languages

We study set-theoretic types: types that include union, intersection, and negation connectives. Set-theoretic types, coupled with a suitable subtyping relation, are useful to type several programming language constructs – including conditional branching, pattern matching, and function overloading – very precisely. We define subtyping following the semantic subtyping approach, which interprets types as sets and defines subtyping as set inclusion. Our set-theoretic types are polymorphic, that is, they contain type variables to allow parametric polymorphism. We extend previous work on set-theoretic types and semantic subtyping by showing how to adapt them to new settings and apply them to type various features of functional languages. More precisely, we integrate semantic subtyping with three important language features. In Part I we study implicitly typed languages with let-polymorphism and type inference (previous work on semantic subtyping focused on explicitly typed languages). We describe an implicitly typed lambda-calculus and a declarative type system for which we prove soundness. We study type inference and prove results of soundness and completeness. Then, we show how to make type inference more precise when programs are partially annotated with types. In Part II we study gradual typing. We describe a new approach to add gradual typing to a static type system; the novelty is that we give a declarative presentation of the type system, while previous work considered algorithmic presentations. We first illustrate the approach on a Hindley-Milner type system without subtyping. We describe declarative typing, compilation to a cast language, and sound and complete type inference. Then, we add set-theoretic types, defining a subtyping relation on set-theoretic gradual types, and we describe sound type inference for the extended system. In Part III we consider non-strict semantics. The existing semantic subtyping systems are designed for call-by-value languages and are unsound for non-strict semantics. We adapt them to obtain soundness for call-by-need. To do so, we introduce an explicit representation for divergence in the types, allowing the type system to distinguish the expressions that are already evaluated from those that are computations which might diverge.
Programming trajectories analytics in block-based programming language learning

ABSTRACT Block-based programing languages (BBPL) provide effective scaffolding for K-12 students to learn computational thinking. However, the output-based assessment in BBPL learning is insufficient as we can not understand how students learn and what mistakes they have had. This study aims to propose a data-driven method that provides insight into students' problem-solving process in a game-based BBPL practice. Based on a large-scale programing dataset generated by 131,770 students in solving a classical maze game with BBPL in Hour of Code, we first conducted statistical analysis to extract the most common mistakes and correction trajectories students had. Furthermore, we proposed a novel program representation method based on tree edit distance of abstract syntax tree to represent students' programing trajectories, then applied a hierarchical agglomerative clustering algorithm to find the hidden patterns behind these trajectories. The experimental results revealed four qualitatively different clusters: quitters, approachers, solvers and knowers. The further statistical analysis indicated the significant difference on the overall performance among different clusters. This work provides not only a new method to represent students' programing trajectories but also an efficient approach to interpret students' final performance from the perspective of programing process.
Exploring the Modularity and Structure of Robots Evolved in Multiple Environments

Traditional techniques for the design of robots require human engineers to plan every aspect of the system, from body to controller. In contrast, the field of evolutionary robotics uses evolutionary algorithms to create optimized morphologies and neural controllers with minimal human intervention. In order to expand the capability of an evolved agent, it must be exposed to a variety of conditions and environments. This thesis investigates the design and benefits of virtual robots which can reflect the structure and modularity in the world around them. I show that when a robot’s morphology and controller enable it to perceive each environment as a collection of independent components, rather than a monolithic entity, evolution only needs to optimize on a subset of environments in order to maintain performance in the overall larger environmental space. I explore previously unused methods in evolutionary robotics to aid in the evolution of modularity, including using morphological and neurological cost. I utilize a tree morphology which makes my results generalizable to other morphologies while also allowing in depth theoretical analysis about the properties relevant to modularity in embodied agents. In order to better frame the question of modularity in an embodied context, I provide novel definitions of morphological and neurological modularity as well as create the sub-goal interference metric which measures how much independence a robot exhibits with regards to environmental stimulus. My work extends beyond evolutionary robotics and can be applied to the optimization of embodied systems in general as well as provides insight into the evolution of form in biological organisms.