Miracle-dz/UniFAR-FTUs huggingface dataset:
Cache-Aided Networks with Shared Caches and Correlated Content under Non-Uniform Demands

In this paper, we propose a cache-aided delivery network with multiple shared caches and correlated content under non-uniform popularity demand. From an information theoretic perspective, we formulate the caching problem as a distributed source coding with side information at the decoder. To address the placement challenge, we propose an automatic clustering scheme considering the popularity and similarity of library content to extract the most efficient side information for caching. Next, we use a hybrid placement strategy, in which the popular side information is fully stored in all caches, while the clusters' side information is partially placed in different caches according to the coded caching strategy (CC). In the delivery phase, the server transmits coded multicast messages and encoded messages (refinement segments) so that users can reconstruct the cluster representatives and clustered files. Our simulation result demonstrates a significant improvement in the peak delivery rate of the system, which resulted from our perspective on problem formulation and the careful extraction of side information during the placement phase.
Real-time Implementation of Semi-active Reconfigurable Intelligent Surfaces for mmWave and Sub-THz Systems

This demonstration proposal presents an effective and energy-efficient reconfigurable intelligent surface (RIS) design and implementation through a real-time beamforming testbed. The RIS can dynamically control the beam tuning property based on liquid crystal (LC) cell structure, and a series of RIS prototypes are devised at 28 GHz and 140 GHz respectively. Actual fabrication and measurement, over-the-air field trial and system-level simulation are performed through this paper, and real-time testbed implementation will be shown to substantiate its feasibility.
Skill Evaluation of Probabilistic Forecasts by the Atmospheric Seasonal Predictability Experiments

Probabilistic forecast skill of the atmospheric seasonal predictability experiments is evaluated using the Japan Meteorological Agency (JMA) Atmospheric General Circulation Model (AGCM), which is a global spectral model of T63 resolution. Four-month ensemble integrations were carried out with nine consecutive days of initial condition preceding the target season. All four seasons in a 15-year period from 1979 to 1993 were chosen as target seasons. The model was forced with observed sea surface temperature (SST) during the time integrations. Probabilistic forecasts of 500 hPa height, 850 hPa temperature and precipitation are verified by four skill measures; the Brier skill score and its decomposition to reliability and resolution, relative operating characteristics (ROC), ranked probability score (RPS) and rank histogram. It is revealed that probabilistic forecast bears some similarity in the seasonality and regionality of skill, with deterministic forecast such as relatively higher skill in winter of the Northern Hemisphere, and over East Asia and North America. Skill of precipitation is found generally lower than that of 500 hPa height and 850 hPa temperature, as is also recognized for deterministic forecast skill.
A Swarm Intelligence Based Community Detection Algorithm in Social Networks

A community consists of a group of dense intra-connected and sparse inter-connected actors. To detect communities in social networks have earned popularity in past few years. We have concentrated on swarm intelligence based community detection in real world networks elaborated in this paper. In computational field, there are several algorithms so far suggested to detect communities in social networks. But most of the existing algorithms take the high running time and less efficient. So, there is a need of an efficient community detection algorithm. Here, a swarm intelligence based proposed method has been elucidated to detect the communities in social networks. We have evaluated algorithm on two measures namely accuracy and Normalized Mutual Information and found that the proposed approach provides efficient results than the Girvan-Newman algorithm. Our proposed method achieves highest accuracy for College Football Network as 84% and performs better in other networks too. Also, 86% NMI score as the highest one is obtained by our proposed method for College Football Network.
Improving SAR-based flood detection in arid regions using texture features

Flood monitoring in arid regions is challenging using Synthetic Aperture Radar (SAR) due to the similar backscatter of water and dry sand in surrounding areas. Since textural information is abundant in SAR images, this study investigates the added value of texture in SAR-based flood detection by providing it as auxiliary information for flood delineation. Results show that texture enhanced SAR images in VH polarization substantially underpredicts the flooded area, so adding texture does not improve the classification accuracy. However, using both polarization (VV and VH) produce $\sim26$% higher overall accuracy for flood detection in arid regions.
A New Approach Based on a Multi–Agent System for IDS in Cloud Computing

Cloud computing is a recent innovation in the IT industry that is expanding quickly. Furthermore, this technology is widely used to provide computation, data storage, and other resources remotely through the web on a pay-per-usage basis. It is now the favored option for any IT firm since it increases its capacity to satisfy the computing requirements of its everyday operations through scalability, mobility, and flexibility at a low price. But there are two key problems with cloud computing. The biggest issue is storage-related, and Google has addressed it by adding a new layer to the cloud dubbed “Big data as a service (BDaaS). ” The second problem is security and privacy. The Intrusion Detection System (IDS) has become the most widely utilized component of computer systems, security, and compliance processes, safeguarding network-accessible Cloud resources and services from various threats and assaults. This study examines IDS approaches in Cloud Computing and big data sets. To identify anomalous data in BDaaS, we also suggested a sensible intrusion detection system (SIDS) based on the autonomic system. The agent contributes the most to introducing more proprieties, particularly the autonomy element.
Research and implementation of obfuscation binary code similarity detection

The problem of binary code similarity detection has made significant progress in malware detection. The comparison of similarity by file bytecode, assembly code, control flow graph, and so on has been applied sufficiently. Nevertheless, the above method must be revised in practical application to judge the similarity of artificially obfuscating binary code. Therefore, this paper proposes a method based on deep learning for binary similarity comparison, which works directly on function disassembly instruction sequences without manual feature extraction. Through the experiment, the improved method can get a good effect on the similarity detection of the binary code which has been obfuscated.
Ameliorating Effect on Aβ-Induced Alzheimer’s Mice by Litsea cubeba Persoon Powder

Alzheimer’s disease (AD) is caused by excessive oxidative damage and aging. The objective of this study was to investigate the anti-dementia effect of LCP fruit powder on amyloid β (Aβ)-induced Alzheimer’s mice. The composition of LCP essential oil was determined by gas chromatography/mass spectrometry. In addition, the water maze was used to evaluate the learning and memorizing abilities of the mice. The concentrations of malondialdehyde (MDA), protein carbonyl, phosphorylated τ-protein, and the deposition of Aβ plaques in mouse brains were also assessed. The results showed that the main components of essential oils in LCP and d-limonene, neral, and geranial contents were 14.15%, 30.94%, and 31.74%, respectively. Furthermore, oral administration with different dosages of LCP significantly decreased the escape time (21.25~33.62 s) and distance (3.23~5.07 m) in the reference memory test, and increased the duration time (26.14~28.90 s) and crossing frequency (7.00~7.88 times) in the target zone of probe test (p < 0.05). LCP also inhibited the contents of MDA and the phosphor-τ-protein from oxidative stress, reduced the brain atrophy by about 3~8%, and decreased the percentage of Aβ plaques from 0.44 to 0.05%. Finally, it was observed that the minimum dosage of LCP fruit powder (LLCP, 30.2 mg/day) could prevent oxidative stress induced by Aβ and subsequently facilitate memory and learning deficits in Aβ-induced neurotoxicity and cognitively impaired mice.
Divide and Cache: A Novel Control Plane Framework for Private 5G Networks

To support a wide range of vertical services in fifth-generation (5G), a concept of private 5G has gained a great attention. Even though several deployment models for private 5G have been reported in the literature, they have not fully utilized the benefit of modularized control plane (CP) network functions (NFs) for better performance and cost efficiency. In addition, their qualitative and quantitative analysis has not been conducted yet. In this paper, we propose a novel CP framework for private 5G dubbed divide and cache (D&C). In D&C, the dependency and frequency of CP NFs are first analyzed based on the 3rd generation partnership project (3GPP) specification. Based on the analysis results, NFs are split into on-premise and edge/public clouds to balance the tradeoff between performance and cost. Also, a new NF called NF profile cache function (NFPCF) is built in on-premise to mitigate the signaling overhead. We implemented D&C over a private 5G testbed using an open-source software (i.e., free5GC). Evaluation results demonstrate that D&C can achieve 19% lower deployment cost at the expense of slightly increased latency compared to the existing model.
Designing and Optimizing an Active Car Suspension with Genetic Algorithm

There has been a lot of advancement in the automotive industry. With more and more electronic intervention, many previously used mechanical systems have been replaced by electrical and electronic ones. Here, to design something useful for the automotive industry which can ensure a more comfortable and safer journey for passengers riding a 4-wheeler vehicle. With all the new models and rising competition in the 4-wheeler market, it is evident that customers want full-ride comfort ensured to them. To simulate the scenario and optimize the points where the bumps affect the movement of the chassis and, in turn, the thorax and pelvis body parts for the passenger in this paper. Genetic Algorithm (GA) to maximize and minimize the suspension to test the effect on the body (Passenger Seat) for the given scenario. GA is an optimization approach which aims to determine such input values in order to obtain the optimal output values or outcomes. The MATLAB and Simulink are used in this paper to carry out the whole of the simulation, including coding.
SHARQ: Scheduled HARQ for Time- and Loss-Rate-Sensitive Networks

5G networks provide high reliability and low delay thanks to Hybrid Automatic Repeat reQuest (HARQ). However, HARQ does not provide predictability for either reliability or delay and residual losses -mostly due to feedback losses and the non-adaptivity of the redundency versions- need to be compensated by the upper layers. We explore the possibility of extending HARQ to the transport layer with a cross-layer implementation that obtains the requirements from the application layer and the channel state information from the lower layers. We propose Scheduled HARQ (SHARQ), a capacity-approaching scheme that uses a graph-based algorithm to find the best repair schedule in polynomial time, thereby approaching the channel capacity much better than unscheduled codes.
Optimization of CNN-based Federated Learning for Cyber-Physical Detection

With the increasing popularity of Cyber-physical Systems (CPS), there is a growing need for efficient and reliable methods for detecting and responding to threats. Federated Learning (FL) is a distributed Machine Learning (ML) technique that can be used to train models on data from multiple devices (i.e., edge devices) while keeping the data local. FL has the potential to improve the security and privacy of data while also reducing the training time and cost. Particularly, CNN-based FL has been shown to be effective for various tasks such as image classification and object detection. However, selecting suitable hyperparameters for constructing local ML models in FL is a significant challenge for practical inference and training on edge devices. In this paper, we focus on the optimization of CNN-based federated learning for the task of cyber-physical detection and we propose employing a novel metaheuristic optimization algorithm called Honey Badger Algorithm (HBA) for tuning the hyperparameters in local ML models (FL-HBA). To show the effectiveness of FL-HBA, we make an evaluation using an intelligent healthcare case study where we consider Sleep Apnea (SA) and use the PhysioNet apnea ECG dataset to diagnose SA. Our results show that the FL-HBA is superior to a Convolutional Neural Network (CNN) baseline, traditional ML techniques, and centralized learning models. Furthermore, we demonstrate that the proposed method for assigning the near-optimal hyperparameter values for centralized learning models improves accuracy by 2%.
Equivalence of Coupled Parametric Oscillator Dynamics to Lagrange Multiplier Optimization

Physical machines can solve optimization problems, employing the maximization or minimization principles that are built into physics. We present a dynamical solver for the Ising problem that is comprised of a network of coupled bistable parametric oscillators and show that it implements Lagrange multiplier constrained optimization. We show that the $2\omega$ pump depletion effect, which is intrinsic to parametric oscillators, automatically enforces the binary Ising constraints. This enables the system's continuous analog variables to converge to high-quality binary solutions to the optimization problem. Moreover, we establish an exact correspondence between the equations of motion for the coupled oscillators and the update rules in the primal-dual method of Lagrange multipliers. Though our analysis is performed using electrical LC oscillators, it can be generalized to any system of coupled parametric oscillators. We simulate the dynamics of the coupled oscillator system on benchmark problems and demonstrate that its performance is comparable to the best-known results.
On Multihop vs. End-to-End Transport

In cellular and WiFi networks, traffic is routed via IP independently of its nature and content, using TCP or variants of TCP as the (connection-oriented) transport layer protocol of choice. In this paper, we present a closer look at the communication theoretical distribution of channel capacities on heterogeneous multihop transmissions. We prove that a multihop perspective can significantly reduce the required redundancy (~ 43% for four segments and ~ 52% for five segments). Our contribution is to further motivate smart network devices and functionalities.
Various Non-Isolated Three Phase grid-integrated PV Inverter Topologies for Leakage Current Reduction - A simulation-based study

Non-Isolated grid-integrated inverter configurations are vastly preferred due to their high efficiency, low cost and compatibility with the system. The main downside of the system is galvanic isolation, leakage current (LC) minimisation, and reactive power compensation. Galvanic isolation and leakage current reduction in non-isolated inverter configuration mainly depends on inverter structure and modulation techniques. Based on these issues, several single-phase grid-integrated inverter configurations are developed and reviewed. Compared to single-phase inverter topologies, there are very few studies on three-phase inverter topologies. Hence, in this paper, several three-phase inverter topologies are reviewed based on AC and DC clamping isolation, hybrid isolation with modified discontinuous pulse width modulation technique on LC reduction, current THD, and the strengths and weaknesses of the structure. Finally, simulations are carried out in MATLAB/Simulink for different inverter topologies.
Implementation of the Data Conversion Function for Wireless Environments for Beyond 5G Applications

Discussions have begun worldwide toward Beyond 5G/6G in the 2030s. In these challenges, the topic of intelligence is a controversial point on various layers of RAN (radio-access-network), core, edge, automation, etc. One of the ideas on that topic is a digital twin platform that integrates physical and cyberspace. This study focuses on the RAN side as the data input to the digital twin. Mainly, this paper proposes a method to obtain accurate coverage data and store it in cyberspace in a dynamically changing coverage environment. Besides, we implement the proposed method and evaluate its measurement results and effectiveness in an outdoor proof-of-concept field.
The annual parallax of cataclysmic binary system AM Herculis with the e-EVN

AM Herculis is a prototype of the class of magnetic cataclysmic variables called "polars". The white dwarf magnetic field is strong enough to synchronize the rotation of the white dwarf with the binary orbit in these close binaries. In such a case only accretion stream is present and the accretion flow is directed onto the magnetic pole (or poles) of the white dwarf. The precise estimation of the distance to polars is crucial in better understanding of physical parameters of these very interesting stellar systems. Here we present results of our astrometric project conducted with the e-EVN at 5 GHz.
A Comparative Assessment of Cutting Techniques for Offshore Energy Structures

This study presents an overall assessment of the current five most conventional cutting technologies in the offshore industry from the economic, production, safety, and environmental impact viewpoints. The applicability, advantages, and disadvantages of each cutting technique for cutting operations of jacket structures are summarised. The cutting times required by different techniques are investigated for decommissioning of jacket structures with different diameters and wall thicknesses. The study provides overall suggestions on the suitability of techniques for cutting offshore foundation structures.
Design and Evaluation of an Application-Oriented Data-Centric Communication Framework for Emerging Cyber-Physical Systems

Emergent Cyber-Physical Systems (CPSs) like VANETs and UAV swarms are expected to fulfill essential roles in critical infrastructure domains. This increasing utility and the present nurturing economic conditions that enable their cost-effective deployment herald a period of significant growth and adoption. In addition, these systems are increasingly required to support complex data-intensive and QoS-sensitive applications in challenging operating conditions. However, the growth of these systems is limited by current Internet protocols that do not comprehensively meet the communication requirements of these systems. In this work, we present the design and evaluation of Software-Defined NAmed-data enabled Publish-subscribe (SNAP) communication framework that can effectively meet the communication requirements of demanding applications in emergent CPSs.
Brain Tumour Segmentation Techniques from MR Images using Machine Learning: An Analysis

One disease kind that targets the brain in the form of clots is a brain tumour. An MRI image is needed in order to see a brain tumour in detail. Because of their similar colours, brain tumours and normal tissue might be hard to tell apart. Accurate research must be done on brain tumours. Segmentation is the answer to analysing a brain tumour. To get around this problem, brain tumour segmentation is used to split the brain tumour made up of various tissues, such as fat, edema, cerebrospinal fluid and normal brain tissue. The MRI image must first the kept at the margin of the image using median filtering. Then the threshold method is needed for the tumour segmentation procedure, which is iterated to take the greatest area. Nowadays, automated disease diagnosis using Magnetic Resonance Images, mammography, and further sources commonly makes use of these CBIR techniques. As a part of the objective of innovation for sustained development, this gap could be closed with the help of our innovative edge detection technique and deep learning feature extraction algorithm, accuracy is now considerably closer to that of manual evaluation by a human.
Functional ANOVA for Modelling Temperature Profiles in Ecuador

The experiments obtained from global and regional climate models have recently become popular and are widely used in the Statistical literature to analyze and understand the evolution of natural phenomena such as climate change in a limited area, using discretized versions of physical processes that they are modeled by systems of differential equations. This methodology is appropriate for the analysis of time series of climatic variables; therefore, the present work is motivated by the need to compare sources of variability in the projections made by the computational models used to model the climate. We propose to use a functional data modeling through an ANOVA, assigning prior distributions of Gaussian processes in each batch of the functional effects. Some alternatives to specify the model will be discussed, and computationally efficient strategies will be considered for the simulation of the samples and estimation of the posterior distribution using a Markov Chain Monte Carlo algorithm for the first case and a Laplace approximation for the second case. The methodology will be illustrated through two cases: the effect of the geographical region on the temperature profiles in the meteorological stations of Ecuador and the sources of variability in the output of regional climate models.
BACKWARD: A Victim-Centric DDoS Detection and Mitigation Scheme in Programmable Data Plane

Most current volumetric DDoS detection and mitigation schemes utilizing data plane programmability are source-based, yet it is challenging to identify an attacker through source analysis because a large number of widespread sources are exploited by the attacker. In this paper, we propose BACKWARD, a victim-centric DDoS attack detection and mitigation scheme that first identifies the victim of the DDoS attack and then only blocks sources that contacted the victim. We implement BACKWARD using the P4 language and present experimental results, which show that BACKWARD is able to achieve higher accuracy in identifying and blocking the attackers compared to the source-based scheme.
Optimizing wireless network throughput under the condition of Physical Layer Security using Software-Defined Networking enabled collaboration

We investigated if independent wireless operators can effectively collaborate using Software-Defined Wireless Networking (SDWN) to achieve secret wireless communications and optimize useful throughput under the condition of Physical-Layer Security enabled secrecy in heavily contended spectrum. We studied this on the level of useful throughput, in addition to Signal-to-Interference-plus-Noise Ratio. The investigation was carried out by means of a series of discrete event simulations of a realistic Wi-Fi network deployment. We found that the difference between the user throughputs of the legitimate station and the eavesdropper could be a reasonably good proxy for secrecy capacity and conclude that SDWN controllers should ideally optimize the network based on empirical feedback from the system and active learning mechanisms, rather than on hard-programmed pre-calculated network models.