Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECTS OF INCLINED CUTOFFS AND SOIL FOUNDATION CHARACTERISTICS ON SEEPAGE BENEATH HYDRAULIC STRUCTURES

If a dam is founded on a pervious foundation, the differential head formed by the dam acts on the foundation and generates under seepage. The seeping flow generates erosive forces which tend to pull soil particles with the flow. This causes the formation of irregular passages like pipes which move beneath the dam. This process is known as piping phenomenon. Piping occurs if the exit hydraulic gradient at the downstream point approaches the critical hydraulic gradient. In order to prevent piping, it is necessary to reduce the velocity of the seeping water to a safe value. This can be accomplished by lengthening the seepage path. One of the methods of such a lengthening is to introduce sheet piles or cutoff walls within the dam foundation. Using different solution methods, such as the conformal analysis, empirical formulas, electrical analog models, experimental works using physical as well as numerical models and the flow net technique many researchers had examined the confined seepage through pervious soils beneath hydraulic structures and studied their safety against piping phenomenon. Estimation of the exit gradient, uplift pressure, and flow rate were provided for various problems including cases of flat bottom dams with one or more embedded vertical sheet piles. However, limited literature is available concerning the use of inclined walls or sheet piles. Moreover, most of these solutions were based on the assumption of a homogeneous and isotropic foundation soil having an infinite depth. Such an assumption is usually not met depending on various in situ conditions that possess anisotropy, heterogeneity, limited soil depth, . etc. It appears that no solution is available in the ready accessible literature to determine the effect of using a flat dam base with either toe or heel sheet pile(s) on seepage flow through heterogeneous and anisotropic soils of limited depths. This problem is solved using the finite element method. A model was prepared to compute the piezometric head distribution for different flow conditions and soil characteristics. The calculated exit gradient values, flow rates, and uplift pressure were shown to be affected by changing the slope angle of the sheet pile and varying the soil and flow conditions.
Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the O(ε-7/4) Complexity

This paper studies accelerated gradient methods for nonconvex optimization with Lipschitz continuous gradient and Hessian. We propose two simple accelerated gradient methods, restarted accelerated gradient descent (AGD) and restarted heavy ball (HB) method, and establish that our methods achieve an $\epsilon$-approximate first-order stationary point within $O(\epsilon^{-7/4})$ number of gradient evaluations by elementary proofs. Theoretically, our complexity does not hide any polylogarithmic factors, and thus it improves over the best known one by the $O(\log\frac{1}{\epsilon})$ factor. Our algorithms are simple in the sense that they only consist of Nesterov's classical AGD or Polyak's HB iterations, as well as a restart mechanism. They do not invoke negative curvature exploitation or minimization of regularized surrogate functions as the subroutines. In contrast with existing analysis, our elementary proofs use less advanced techniques and do not invoke the analysis of strongly convex AGD or HB. Code is avaliable at https://github.com/lihuanML/RestartAGD.
Empregando modelo de interação na representação de padrões de usabilidade

Abstract. Design patterns are widely used in software development since the original proposal of a catalog of reusable object-oriented solutions - the GoF patterns. They have been applied successfully also in software process and user interface and interaction design. One of the main reasons of its popularity is because it is possible to represent the tacit design knowledge that has been revealed in design practice. However, differently as in GoF patterns, GUI and WUI patterns are not represented using visual diagrams, which has been the lingua franca for most software development stakeholder. Beyond that, visual languages allow usability and architectural impact analysis at design time. In this paper, we show how design patterns can be represented in ALaDIM, an interaction design language. We also argue that the integration of interaction patterns and visual languages enhances the collaboration among HCI and software engineering professionals.
CoWatch: Collaborative Prediction of DDoS Attacks in Edge Computing with Distributed SDN

With the development of Edge Computing (EC), security issues have raised concerns. Due to the unusual vulnera-bility of EC servers and the distributed nature of attack sources, it is a great challenge to efficiently and effectively defend against DDoS attacks. Existing detection solutions, based on the feedback of servers under the attacks, can incur high bandwidth costs and degradation of service performance. To address this problem, we propose a novel collaborative prediction framework, called CoWatch. Based on the distributed software-defined network (SDN), the CoWatch framework can collaboratively predict the DDoS attacks towards the EC servers and detect the attack flows near the attack source in time. To efficiently filter the suspicious flows in distributed SDN, we design an optimal threshold model by balancing the trade-off between collaboration efficiency and prediction effectiveness. We also explore the prototypical LSTM network to design an LSTM-based Collaborative Prediction (LCP) algorithm, which can effectively predict and detect DDoS attacks. Experiment results demonstrate the effectiveness of the prediction and detection of DDoS attacks and validate the efficiency of flow information synchronization in distributed SDN.
Delay Aware Secure Computation Offloading in NOMA aided MEC for IoV Networks

In this paper, we investigate a multi-vehicle multi-task non-orthogonal multiple access (NOMA) based mobile edge computing (MEC) system with passive eavesdropping vehicles. To enhance the performance of edge vehicles, we propose a vehicle grouping and pairing method (GPM) to employ the vehicle near to MEC acting as a full-duplex (FD) relay to assist edge vehicles. In order to improve the transmission security, artificial noise (AN) is used to interfere with eavesdropping vehicles. The approximate expression of secrecy outage probability of the system (SOPS) is derived in closed form. This paper aims to minimize the total delay of task completion of edge vehicles by jointly optimizing vehicle task division, power allocation and transmit beamforming. We design a power allocation and task scheduling algorithm relying on genetic algorithm (GA-PATS) to solve our formulated mixed-integer non-linear programming (MINP) problem. Numerical results demonstrate the superiority of our proposed scheme in the aspect of system security and transmission delay.
Block-Sparse Channel Estimation in Massive MIMO Systems by Expectation Propagation

We consider downlink channel estimation in massive multiple input multiple output (MIMO) systems using a Bayesian compressive sensing (BCS) approach. BCS exploits the sparse structure of the channel in the angular domain in order to reduce the pilot overhead. Due to limited local scattering, the massive MIMO channel has a block-sparse representation in the angular domain. Thus, we use a conditionally independent and identically distributed spike-and-slab prior to model the sparse vector coefficients representing the channel and a Markov prior to model its support. An expectation propagation (EP) algorithm is developed to approximate the intractable joint posterior distribution on the sparse vector and its support with a distribution from an exponential family. The unknown model parameters which are required by EP, are estimated using the expectation maximization (EM) algorithm. The proposed combination of EM and EP algorithms is reminiscent of variational EM and is referred to as EM-EP. The approximated distribution is then used for estimating the massive MIMO channel. Simulation results show that our proposed EM-EP algorithm outperforms several recently-proposed algorithms in channel estimation.
Drone-Aided Network Coding for Secure Wireless Communications: A Reinforcement Learning Approach

This study investigates how base stations (BSs) apply network coding to protect the downlink data and how drones relay the coded packets to resist active eavesdropping that performs jamming to induce the BS to raise the transmit power and thus steal more data. We present a drone-aided network coding framework for secure downlink transmission, which incorporates a random linear network coding algorithm to encode the BS messages against active eavesdropping. This framework designs a model-based reinforcement learning to choose the BS network coding and transmission policy based on the jamming power sent by the active eavesdropper, the previous transmission performance, and the BS channel states without the prior knowledge of the drone-eavesdropper channel states. The learning parameters such as the Q-values are updated by the real experiences in the downlink transmission process besides the simulated experiences that are generated from the virtual model in the designed Dyna architecture. Simulation results show that our proposed scheme outperforms the benchmarks in terms of the intercept probability, the transmission performance, and the BS energy consumption.
Performance Analysis of Transversal Filter Combination for Feedback Suppression in Hearing Aids

This paper investigates the mean square performance of two transversal adaptive filters for the feedback cancellation model in digital hearing aids. This convex combination allows the update has to be performed independently with their errors generated during the adaptation process. The Gradient descent stochastic algorithm is considered to minimize the overall system error with the help of the mixing parameter $\delta(n)$. The complete analytical expressions of the combination have been derived and validated with the simulation results in the stationary environment. The proposed combination scheme shows better feedback cancellation performance, as illustrated in the results.
Study of molecular recognition mechanism in protein GPI modification: a bioinformatics analysis of interaction between GPI-anchored proteins and modification enzyme

The interaction for protein glycosylphosphatidylinositol (GPI) modification between the premature GPI-anchored proteins and the active sites in PIG-K, one of the GPI transamidase proteins, is discussed in this study by the homology modeling method and amino acid propensity analysis in the space around the -sites in premature GPI-anchored proteins. In particular, the direct interaction between -sites of GPI-anchored proteins and PIG-K was focused on, the root-mean-square deviation (RMSD) and three-dimensional amino acid propensity around the -sites were analyzed in the present study. As the results, PIG-K was considered to recognize the specific structure around the -sites of the GPI-anchored proteins, the positively-charged Lys (K) and Arg (R) residues around the -sites have the possibility to interact with the negatively-charged Asp (D) and Glu (E) residues around an active site of PIG-K, and Tyr (Y) and Ala (A) residues around the -sites are thought to be essential for molecular recognition by PIG-K. The findings in this study that structural recognition around the -site in the mature GPI-anchored proteins by PIG-K are useful for understanding the local mechanism of the GPI modification enzyme and can be applied for the development of cell-surface-engineering.
Beamforming and Power Allocation in NOMA-Based Multibeam Satellite Systems with Outage Constraint

In this paper, we propose a joint beamforming (BF) and power allocation scheme for non-orthogonal multiple access based multibeam satellite systems. Unlike the existing works where perfect channel state information (CSI) is required, we use imperfect CSI in formulating a constrained optimization problem aiming to minimize the maximum individual antenna powers subject to the outage constraints of quality-of-service requirements. Since the original problem is mathematically intractable, we first adopt Bernstein-Type II inequality to convert the outage constraints into deterministic forms. Then, an alternating optimization algorithm is proposed to jointly design BF vectors and power allocation coefficients. Finally, simulation results are provided to demonstrate the robustness and superiority of the proposed scheme compared with benchmark schemes.
TPA based content popularity prediction for caching and routing in edge-cloud cooperative network

The rapid development and application of 5G/B5G generate tremendous amount of traffic which in turn cause great burden for the corresponding transmission network. One typical way to address such challenge is to sink the content (e.g., 4K and 8K videos) from the remote cloud to the edge servers. In this case, how to efficiently visiting and getting these contents becomes a new problem, in which the cooperation between cloud and edge should be taken into consideration. In this regard, this work builds an edge and cloud cooperative routing and caching system which consists of three main modules of content popularity prediction, cooperative caching and cooperative routing. Specifically, the content prediction is designed by jointly leveraging the technologies of Long Short-Term Memory (LSTM) and Temporal Pattern Attention (TPA) to dig the traffic features and predict the future content popularity. Based on the prediction results and the technology of reinforce learning, the cooperative caching module designs both a reactive content replacement and an active content caching strategies. After that, the cooperative routing is carried out to help customers visiting and obtaining these content efficiently with the objective of minimizing the overhead. The experimental results indicate that the proposed methods outperform the state-of-the-art benchmarks in terms of the caching hit rate, the average throughput, the successful content delivery rate and the average routing overhead.
IGRAND: decode any product code

We introduce Iterative GRAND (IGRAND), a universal product code decoder that applies iterative bounded distance decoding and decodes component codes using code-agnostic Guessing Random Additive Noise Decoding (GRAND). We empirically determine its accuracy and, based on GRAND hardware measurements, its complexity, showing gains over alternative algorithms. We prove that the class of product codes with random linear component codes, which IGRAND is capable of decoding, are capacity-achieving in hard-decision channels.
MAFENN: Multi-Agent Feedback Enabled Neural Network for Wireless Channel Equalization

Feedback mechanism has been widely used in wireless communication such as channel equalization and resource allocation. In recent years, deep learning (DL) has made great progress in the field of wireless communication. There is now some work that attempts to introduce plain feedback mechanisms into DL algorithm to solve wireless communication problems. However, the improvement of plain feedback DL methods is limited in complex situations due to those methods lack sufficient learning ability on feedback information. In this paper, we propose a Multi-Agent Feedback Enabled Neural Network (MAFENN) equalizer, which consists of a specific learnable feedback agent and two feed-forward agents. Three fully cooperative intelligent agents help the system improve the ability to remove wireless inter-symbol interference (ISI) in receiving ends. We further formulate it into a three-player Stackelberg Game, which helps us to optimize and train this model more efficiently. To verify the feasibility of our proposed MAFENN system and the Stackelberg Game optimization, we conduct a series of experiments to compare the symbol error rate (SER) performance of the MAFENN equalizer and the other methods which utilizes quadrature phase-shift keying (QPSK) modulation scheme. Our performance outperforms that of the other equalizers at different signal-to-noise ratio (SNR) settings for both linear and nonlinear channels.
Nonlinear model predictive control applied to robust guidance of autonomous surface vehicles

This paper proposes a nonlinear model predictive control-based guidance strategy for autonomous surface vehicles, focused on the path following approach to motion control. The application of this strategy, in addition to overcome the drawbacks of previous line-of-sight-based guidance laws, intends to enable the application of predictive strategies also to the low-level control, responsible for tracking the references provided by the guidance high-level strategy. The robust and stable features of the proposed strategy are discussed, while the effectiveness and the advantages over other nonlinear guidance laws are illustrated through a complete set of simulations.
Optical Fiber Fabry-Perot based Spirometer for Pulmonary Health Assessment: Concept Evaluation

In this paper, we present an optical fiber based approach to accurately evaluate the respiratory capacity of individuals through spirometry. The proposed system converts the air flow rate into strain variations in an optical fiber, which will modulate the spectral response of a Fabry-Perot interferometer (FPI) micro-cavity, created in the fiber core. Simulation studies were performed to verify the proposed theoretical approach. Additionally, preliminary results concerning the study of the system's reliability are also presented, which confirm the suitability of using FPI sensors for the evaluation of spirometry parameters. The simulation and implementation results showcase the accuracy of the measurements performed by the proposed solution, confirming its appropriateness to be used as a smart low-cost eHealth spirometer. This is the first time such FPI approach is used with this purpose.
FedGR: A Lossless-Obfuscation Approach for Secure Federated Learning

Federated learning is a promising new technology in the field of artificial intelligence. However, the unprotected model gradient parameters in federated learning may reveal sensitive participants information. To address this problem, we present a secure federated learning framework called FedGR. We use Paillier homomorphic encryption to design a new gradient security replacement algorithm, which eliminates the connections between gradient parameters and user sensitive data. In addition, we revisit the previous work by Aono and Hayashi(IEEE TIFS 2017) and show that, with their method, the user's local computing burden is too heavy. We then proved FedGR has the following characteristics to solve this problem: 1) The system does not leak any information to the server. 2) Compared with that of ordinary deep learning systems, the accuracy of federated training results yielded by our system remains unchanged. 3)The proposed approach greatly reduces the user's local computing overhead.
Photonic sensors for non-invasive home monitoring of elders

In this paper, we present an optical fiber based architecture for non-invasive home monitoring of elder citizens. The approach is based on a network of optical fiber sensors distributed along the space/room to be monitored. The sensing mechanism is based on optical fiber Bragg grating (FBG) sensors, produced by the phase mask method and integrated within an accelerometer structure. This type of sensing solution has high sensitivity, allied with an extra resilience. Here we present the proposed architecture, the evaluation of different parameters that influence the accelerometer feedback, and the theoretical approach for indoor localization using this type of sensing mechanism. One advantage of the proposed solution is that it does not depend on wearables, which are considered burden for elders.
Trading-off safety, exploration, and exploitation in learning-based optimization: a Set Membership approach

We propose a technique for global optimization considering black-box cost function and constraints, which have to be learned from data during the optimization process, arising for example in plant-control co-design of complex systems or controller tuning based on experiments. Assuming Lipschitz continuity of the cost function and constraints, we build a surrogate model and derive tight bounds on such functions based on a Set Membership framework. An exploitation step is designed to improve on the current best feasible candidate solution, searching in regions where all constraints are estimated as fulfilled, thus preserving safety. On the other hand, an exploration routine aims to discover the shape of the cost and constraint functions by picking points with large uncertainty, prioritizing regions where more constraints are predictably satisfied. The proposed algorithm can intuitively trade-off safety, exploration, and exploitation. The performance is evaluated on the problem of model predictive control tuning for a servomechanism with plant uncertainties and task-level constraints.
Taxonomy of Machine learning techniques and elastic resource optimization in sliced networks

With the diversification of 5G services especially the emergence of applications related to the Internet of Things (IoT), slicing is considered as the best way to offer the optimal Quality of Services (QoS) and Quality of Experience (QoE) using the same shared infrastructure. In this paper, we focus on the optimal resource reservation using recent methods and techniques in order to meet the highly variable and dynamic demands. That's why, we propose a taxonomy of different approaches using Artificial Intelligence/Machine Learning (AI/ML) for slicing resource optimization and orchestration. In addition, a comparison study of different solution is proposed. Finally, we highlight future challenges and discuss future research directions.
Nonlinear Dual control based on Fast Moving Horizon estimation and Model Predictive Control with an observability constraint

This paper proposes an algorithm that combines Fast Moving Horizon Parameter Estimation and Model Predictive Control subject to an observability constraint designed to ensure a lower bound on the performance of the parameter estimator. Output-feedback stability is proved through input-to-state stability of the state/error system under a small noise and initial error assumption. Numerical experiments have been carried out in the case of Active Simultaneous Localisation and Mapping (SLAM).
Half Substrate Integrated Waveguide Based Wideband Slot Antenna

A low profile wideband slot antenna based on the half substrate integrated waveguide (SIW) cavity is analyzed, proposed for various applications in X-band. Initially, an H-shaped slot-loaded SIW cavity-backed antenna is analyzed where horizontal and vertical slot lengths are governing the dual resonant frequencies. Subsequently, the H-shaped slot is modified along with the horizontal slot for the wideband performance. Further, the half SIW cavity antenna with the modified slot is realized to obtain the small size and wideband operation. The designed antenna is fabricated and tested for the desired performance. A relative impedance bandwidth of 11.8% (10.48GHz-11.8GHz) along with low cross polarization level is attained. The SIW cavity helps in achieving a broadside radiation along with the peak realized gain of 5.18dBi and 5.77dBi at the two working frequencies 10. 8GHz and 11.41GHz, respectively. The suggested wideband antenna makes it a strong choice for various applications like radar guns, air traffic control, defense tracking, and weather monitoring.