Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Routing in Challenged Networks with Graph Neural Networks

Challenged networks are characterized by a time-varying operational environment that constrains the optimality of standard routing algorithms. This work investigates a deep learning method that tackles the bundle routing problem by taking advantage of the available network metrics and performance data through a graph neural network (GNN). A cognitive routing decision unit is formulated by defining a GNN structure that accepts both edge and node input features, and that is trained with reinforcement learning. The GNN allows the inputs to be permutation invariant and independent of the network size and connectivity. Simulation results demonstrate that the proposed cognitive routing method is able to learn how to optimize the next-hop for each data bundle of a flow to achieve lower end-to-end delivery latency than the standard Contact Graph Routing algorithm. The GNN achieves the optimization by detecting and avoiding the extended wait times caused by both butter congestion and the stall times for the next contact when long link disruptions occur.
ImageSpec: Efficient High-Level Synthesis of Image Processing Applications

The necessity of efficient hardware accelerators for image processing kernels is a well known problem. Unlike the conventional HDL based design process, High-level Synthesis (HLS) can directly convert behavioral (C/C++) description into RTL code and can reduce design complexity, design time as well as provide user opportunity for design space exploration. Due to the vast optimization possibilities in HLS, a proper application level behavioral characterization is necessary to understand the leverages offered by these workloads especially for facilitating parallel computation. In this work, we present a set of HLS optimization strategies derived upon exploiting the most general HLS influential characteristic features of image processing algorithms. We also present an HLS benchmark suite ImageSpec to demonstrate our strategies and their efficiency in optimizing workloads spanning diverse domains within image processing sector. We have shown that an average performance to hardware gain of 143x could be achieved over the baseline implementation using our optimization strategies.
A Parasitic Microstrip Linear Array With Customizable Tilted-Beam Radiation

A single-layer microstrip parasitic array with customizable tilted-beam radiation performance is presented. The antenna consists of one driven sub-wavelength patch and eight parasitic patches and the tilt angle is determined by the refractive index distribution of the patch units. The index-modulation principle is derived based on the generalized law of refraction. According to this principle, the size of the patch unit can be adjusted to control the tilt angle. This paper uses I-shaped patch unit to conFigure the tilted-beam antenna. Simulation results manifest that the antenna has a large tilt angle of 60° off boresight and a broad bandwidth of 19.31%.
Shepherd: Seamless Stream Processing on the Edge

Next generation applications such as augmented/vir-tual reality, autonomous driving, and Industry 4.0, have tight latency constraints and produce large amounts of data. To address the real-time nature and high bandwidth usage of new applications, edge computing provides an extension to the cloud infrastructure through a hierarchy of datacenters located between the edge devices and the cloud. Outside of the cloud and closer to the edge, the network becomes more dynamic requiring stream processing frameworks to adapt more frequently. Cloud based frameworks adapt very slowly because they employ a stop-the-world approach and it can take several minutes to reconfigure jobs resulting in downtime. In this paper, we propose Shepherd, a new stream processing framework for edge computing. Shepherd minimizes downtime during application reconfiguration, with almost no impact on data processing latency. Our experiments show that, compared to Apache Storm, Shepherd reduces application downtime from several minutes to a few tens of milliseconds.
Predictability of Precipitation from Continental Radar Images. Part IV: Limits to Prediction.

Abstract Predictability of precipitation is examined from storm to synoptic scales through an experimental approach using continent-scale radar composite images. The lifetime of radar reflectivity patterns in Eulerian and Lagrangian coordinates is taken as a measure of predictability. The results are stratified according to scale, location, and time in order to determine how predictability depends on these parameters. Three companion papers give a detailed description of the methodology, and present results are obtained for 143 hours of North American warm season rainfall with emphasis on lifetime, scale dependence, optimum smoothing of forecast fields, and predictability in terms of probabilistic rainfall rates. This paper discusses the sources of forecast uncertainty and extends the analysis to a total of 1424 hours of rainfall. In a Lagrangian persistence framework the predictability problem can be separated into a component associated with growth of precipitation and a component associated with change.
3 keys to ensure the sustainability of your solar power plant

In the context of climate change, green energies have an important place. Solar energy has gradually taken its place in the global energy mix, and investments in the operation of solar photovoltaic plants have been stimulated by a favourable market, despite the health and geopolitical crises. How can we ensure that these investments are sustainable in the sense of the United Nations’s Sustainable Development Goals? This is the objective of our study. We assessed the sustainability of large-scale, grid-connected photovoltaic solar power plants by conducting six case studies in six countries on the African continent to determine the key elements. Based on internal and external variables acting on the sustainability of on-grid solar power plants at various levels, three key areas of consideration emerged: (i) evaluation - using appropriate and qualitative evaluation tools, (ii) integration - opting for an integrated approach in the development of the investment project, and (iii) responsibility - not underestimating the corporate social responsibility in the local approach.
Modelling Temperature Field in Spatial Thermo-Sensitive Elements of Electronic Devices With Local Thermal Heating

A non-linear mathematical model for the determination of the temperature field, and subsequently for the analysis of temperature regimes in an isotropic spatially heat-sensitive heterogeneous environment, which is subjected to an external heat load, has been developed. This model was linearized using the Kirchhoff transformation, as a result of which the Fourier integral transformation method was applied to the obtained linear boundary value problem and an analytical solution was obtained that determines the Kirchhoff linearizing function. As an example, the linear dependence of the coefficient of thermal conductivity of structural materials of the structure on temperature, which is often used in many practical problems, is chosen. As a result, an analytical relation in the form of a nonlinear equation was obtained for determining the temperature distribution in a thermosensitive half-space with a foreign inclusion during external local heating. Numerical analysis of temperature behavior as a function of spatial coordinates for given values of geometric and thermophysical parameters was performed.
Detection of PCB surface defects based on a compressed target detection model

PCB is an important component of electronic products, which is critical in the quality of production. But It has those problems including low efficiency of current PCB surface defect detection methods and high equipment cost caused by a large deep learning model. A method involves combining simplified YOLOv5s network with model channel pruning through the follow three steps. First of all it's based on the characteristics of relatively small defects in PCB manufacturing, and removes the deep convolution feature map and detection scale of the YOLOv5s model. Secondly, sparsely trains the BN layer scaling factor of the model through L1 regularization. Finally, the feature extraction network is pruned and fine-tuned. After the above steps, the experimental results show us, under sparse training and 65% channel pruning rate, the performance of the fine-tuning model is better, speeded up and improved. For example the accuracy of PCB defect detection was 96.39%, the volume was 7.9 MB and the speed was 54.3f/s, including deployability and applicability.
Distributed Control Microgrids: Cyber-Attack Models, Impacts and Remedial Strategies

Microgrids are rapidly evolving as cyber-physical systems to have interoperability among the connected sources. These cyber-physical microgrids (CPM) are highly dependent on communication networks for realizing power control strategies, making them highly vulnerable to cyber intrusions. Cyber-attacks are usually intended to meddle with data availability, integrity, and confidentiality. This paper first summarizes mathematical models of commonly occurring cyber-attacks, which include denial of service, false data injection, replay, and covert attacks. Further, a brief discussion about their impacts on distributed controlled AC and DC CPM is detailed. As consensus among the sources is an essential requirement in distributed CPM, the technical and physical challenges arising in achieving the consensus due to different attacks in AC and DC microgrids are analyzed using simulation case studies. Viewing the importance of security assessment and enhancement, state-of-the-art cyber defense strategies for AC and DC CPM are discussed. For better understanding, few defense methods are simulated and validated on CPM. Finally, this paper identifies a few challenges in security and presents some basic resilient strategies along with future research scope in this field.
Exploring Statistics Around Kahn-Kalai Conjecture by Using Parallel Algorithms

There are many questions about the statistical properties of random graphs, particularly those related to cyclic structures. However, theoretical advances have been made in the sparse connection regime. Recent results on the Kahn-Kalai conjecture show that there is a limiting connection probability beyond which it's very likely to find Hamiltonian cycles. It is shown that this probability is $P \sim log(n)/n$ where $n$ is the number of nodes. We explore experimentally around this limit by showing its empirical statistical behavior. These results are useful in configuring various engineering problems based on sparse graphs.
Poster: Scalable Quantum Convolutional Neural Networks for Edge Computing

The convolutional neural network (CNN) has become a general approach for image processing in machine learning tasks. Quantum CNN (QCNN) is an emerging method to implement CNN using quantum computing. Quantum computing utilizes the properties of quantum mechanics to perform efficient computing. However, current quantum machines do not support large-scale QCNNs due to a lack of qubits. As a consequence, QCNNs are limited in scale and cannot directly process high-dimensional images. These shortcomings result in suboptimal QCNN performance. Meanwhile, building quantum machines with enough qubits is technically difficult and costly. These obstacles motivate us to design a quantum edge computing (QEC) system capable of achieving the scalability of QCNNs. Quantum machines are organized hierarchically in the QEC system. The quantum machines closer to the users collaboratively load and extract quantum features from the high-dimensional input data. Subsequently, the quantum machine in the next layer collects the extracted features and performs further operations to produce the final results. Each quantum machine in the QEC system is equipped with a local small-scale QCNN to capture the data pattern of its input. The local QCNNs could be combined to form a large-scale QCNN capable of learning and processing high-dimensional data, overcoming hardware limitations and improving performance.
Fault Detection Based on Least Squares with Limited Samples

In this paper, a data-driven method using least squares is proposed for finite training samples, with a focus on parameter identification and the corresponding performance evaluation. In the procedures of parameter identification, “probably approximately correct” (PAC) is used for determining the confidence of the obtained the modeling error that affects Fault Detection (FD) performance. The main contributions of this paper are: 1) The modeling error expressed by infinite norm norm can be reduced by increasing the size of training data; 2) the sub-Gaussian noise is taken into account; 3) the difference between system outputs and their predictions is used as the residual signal, based on which test statistics are defined to obtain FD results. Finally, the effectiveness of the proposed method is demonstrated through rigorous mathematical derivation and case studies.
Design of an Electronic Nose System for Exhaled Gas Markers of Acute Kidney Injury Patients

In order to obtain the information of exhaled gas marker concentration of acute kidney injury patients quickly and accurately, a quantitative VOCs detection system based on metal oxide gas sensors array and BP neural network model is proposed in this paper. Firstly, four kinds of metal oxide gas sensors are used to form a sensor array to convert the measured gas components and concentrations into electrical signal waveforms, and the feature matrix is obtained by feature extraction of the sensor array's signals. Then, a BP neural network model is used to predict the target gas concentrations, and the topology design and parameters of the neural network are optimized.
ExG Signal Feature Selection Using Hyperdimensional Computing Encoding

Wearable IoT devices and novel continuous monitoring algorithms are essential components of the healthcare transition from reactive interventions focused on symptom treatment to more proactive prevention, from one-size-fits-all to personalized medicine, and from centralized to distributed paradigms. HyperDimensional Computing (HDC) is an emerging ML paradigm inspired by neuroscience research with various aspects interesting for IoT devices and biomedical applications. In this work, we explore five HD vector encoding strategies of spatio-temporal ExG data, such as that of electroencephalogram (EEG), and test it on a use case of epileptic seizure detection. We discuss the impact of these strategies’ performance, memory overhead, and computational complexity. Furthermore, we demonstrate how feature selection via the HDC framework can be accomplished by choosing a proper encoding, and results in up to 70% reduction in used features while improving performance up to 7%.
Fault detection observer-based controller design for 2D continuous switched systems

This paper deals with the problem of fault detection and state estimation in an observer-based controller framework for two dimensional (2D) continuous time switched systems described by Roesser model. The aim of this paper is to estimate the state and sensor fault and to stabilize the 2D system by an observer-based controller where the estimation of the state is used by the controller. The Lyapunov theory is then applied to establish two conditions in the form of matrix inequalities allowing the design of the observer-based controller. The two conditions have to be solved sequentially since the observer condition depends on the solution of the controller condition. A numeric example is provided in order to illustrate the effectiveness of our results.
MPC-Based Reference Governor Control for Self-Righting of Quadruped Robots: Preliminary Results

Even with the current efficient legged robots' control techniques, unexpected situations may occur, leading the system to a fall. For the robot to recover mobility, it must be capable of autonomously repositioning itself. The named Self-Righting task aims to solve the problem of robot mobility recovery with a set of reference poses. This work proposes using a Reference Governor Control (RGC) for a quadrupedal robot's stand-up sub-task in a closed-loop scheme. The RGC is composed of a predictive controller based on the robot's states and the constraints' forces obtained from its interaction with the environment. From the results, it is possible to observe that the proposed solution can reposition the robot to a fully recovered mobility state.
Modeling Misinformation With Q-Learning, Nim, and Multi-Agents

Misinformation or so called ”fake news” has become a pressing issue around the world. This research proposes modeling the spread of misinformation through Q-learning, the game of Nim, and multi-agent simulations. Through analyzing theoretical properties of the model and studying factors in how misinformation affects it, we show how the model reflects the real-life impact of fake news. The scalability of the framework and its emulation of real-human behavior make it versatile and believable. Through further study of this model, our hope is to shed light on effective tactics for combating fake news in the real world.
Branches of stable three-tori using Hamiltonian methods in hopf bifurcation on a rhombic lattice

This paper uses Hamiltonian methods to find and determine the stability of some new solution branches for an equivanant Hopf bifurcation on C^4. The normal form has a symmetry group given by the semi-direct product of D_2 with T^2 S^1. The Hamiltonian part of the normal form is completely integrable and may be analyzed using a system of invariants. The idea of the paper is to perturb relative equilibria in this singular Hamiltonian limit to obtain new three-frequency solutions to the full normal form for parameter values near the Hamiltonian limit. The solutions obtained have fully broken symmetry, that is, they do not lie in fixed point subspaces. The methods developed in this paper allow one to determine the stability of this new branch of solutions. An example shows that the branch of three-tori can be stable.
UDA-CT: A General Framework for CT Image Standardization

Large-scale CT image studies often suffer from a lack of homogeneity regarding radiomic characteristics due to the images acquired with scanners from different vendors or with different reconstruction algorithms. We propose a deep learning-based framework called UDA-CT to tackle the homogeneity issue by leveraging both paired and unpaired images. Using UDA-CT, the CT images can be standardized both from different acquisition protocols of the same scanner and CT images acquired using a similar protocol but scanners from different vendors. UDA-CT incorporates recent advances in deep learning including domain adaptation and adversarial augmentation. It includes a unique design for model training batch which integrates nonstandard images and their adversarial variations to enhance model generalizability. The experimental results show that UDA-CT significantly improves the performance of the cross-scanner image standardization by utilizing both paired and unpaired data.
Distributed spectrum sensing and access through deep recurrent Q-networks

Dynamic spectrum access (DSA) has gained significant prominence for spectrum utilization. In DSA avoiding collision with the PUs and maintaining coordination with other SUs are the critical problems. These problems become severe in a distributed scenario, where no centralized controller is utilized. In this paper we propose a deep recurrent Q-network (DRQN) based approach for distributed sensing and access. In the proposed approach, the SUs will learn strategies in a distributed manner with having the system dynamics. Moreover, we utilized a special type of recurrent neural network (RNN) called long short-term memory (LSTM) to realize the deep reinforcement learning (DRL). The proposed approach is a combination of DRQN and LSTM, in which SUs will take decision independently. The simulation results present the suitability of proposed approach to handle the dynamic environment. The proposed approach achieves similar results in terms of reward value, and it further provides improvement in the collision number compared to the other techniques.
Alignment of Dynamic Networks based on Temporal Embeddings

In the real-world systems, the interactions between objects (e.g., molecules) are generally represented through the dynamic networks, based on a graph-model that evolve over the time. Network alignment allows evaluating different networks in terms of homology and topology. It is a method to map nodes from different networks, in order to match the same entities. For instance, we can assume that the topological similarity between the regions of two given networks corresponds to their functionality, e.g., in terms of biological process. Therefore, the alignment between the biological networks of two species may be useful to transfer the knowledge from the simplest (e.g., mouse) to the more complex (e.g., human). In this paper, we present DANTE (DynAmic Networks alignment based on Temporal Embeddings), a solution for the alignment of dynamic networks, based on temporal embeddings. DANTE takes into account the similarity between the nodes based on their evolution and relationships between one time point and the subsequent, for all ones in the networks. Our temporal embeddings concern a set of vectors representing the topological similarity between the nodes of two given networks, by representing each node as a word on the Skip-Gram model. In addition, we extend the embeddings process to the dynamic networks, in order to evaluate the similarity between two dynamic networks. DANTE applies an iterative process for maximizing globally the match score between the pair of nodes.DANTE is freely available on https://github.com/pietrocinaglia/dante.
S3 AAL: Support Set Selection based on Adversarial Active Learning for Medical Few-Shot Relation Extraction

Support set is one of the most important components of Few-Shot Learning (FSL) methods that greatly affects the performance of these methods. Most existing studies mainly focus on how to effectively utilize the support set sampled randomly, but ignoring the representative of the support set, leading to that the performance of the few-shot learning methods using different support sets randomly sampled varies greatly. In this paper, we focus on how to select a representative support set for FSL methods for medical few-shot relation extraction (FSRE), and propose a novel approach for Support Set Selection based on Adversarial Active Learning $(\text{S}^{3}$ AAL). The adversarial active learning does not only keeps the features shared by source and target, but also guarantees the diversity of the support set. We create three benchmark datasets for medical FSRE based on four public medical RE datasets. The experimental results on the three benchmark datasets demonstrate the effectiveness of our approach when it is plugged into state-of-the-art (SOTA) few-shot learning methods.
Specific Aspects of Software Development Process for AI/ML-based Systems

AI/ML-based software systems have spread in different business domains because they provide solutions to various problems. But the development process requires performing additional activities and incorporating additional team members’ skills. It creates new challenges for software engineers. We described the core differences in the software development process for AI/ML-based systems and the known approaches to working with them.
Color and cyber‐attractiveness: Red enhances men's attraction to women's internet personal ads

Red is traditionally connected with love and sex. However, the effect of red on human behavior still remains in question. Women with Internet personal ads registered on a web meeting site displayed photographs with their upper clothes colored in red, black, white, yellow, blue, and green. The dependent variable was the number of contacts received from men. It was found that women's ads with red received significantly more contacts. © 2012 Wiley Periodicals, Inc. Col Res Appl, 38, 309–312, 2013