Miracle-dz/UniFAR-FTUs huggingface dataset:
User-Centric Network Architecture Design for 6G Mobile Communication Systems

In conventional mobile communications systems, network services are designed to serve a huge amount of subscribers simultaneously, which is normally called a network-centric design approach. In comparison, this paper aims to investigate the user-centric design approach, which refers to sys-tems that are designed to be user-defined, user-configurable and user-controllable. The user-centric approach allows for dedicated network services to be provided at the granularity of the user. A novel User-Centric Network (UCN) architecture is proposed in this work, which includes key design principles, corresponding network elements as well as procedures. It is envisioned that UCN is distributed in nature by leveraging enabling technologies like Distributed Ledger Technology (DLT) and Distributed Hash Table (DHT). In this way, UCN not only provides extreme customization by offering fine-grained services, but also enables autonomous and trusted data control and privacy protection. A simulation platform is developed to verify the feasibility of the architecture, and to preliminarily evaluate its performance by numerical results in terms of hop count, bandwidth consumption, latency, success ratio and scalability.
A mm-Wave Blocker-Tolerant Receiver Achieving <4 dB NF and −3.5 dBm B1dB in 65-nm CMOS

Digital beam-forming requires highly linear receivers (RXs), as null steering is performed only in digital baseband (BB). This paper presents a highly linear RX, with a high out-of-band (OOB) blocker tolerance without sacrificing performance or power, utilizing a highly linear inverter low-noise amplifier (LNA), followed by an N-path mixer with tunable filtering properties and a BB transimpedance (TIA) amplifier for linearity enhancement. The N-path mixer design trade-offs are discussed, and several linear LNA topologies are presented and compared. A chip prototype was manufactured in a TSMC 65 nm CMOS process. In our implementation, a <4 dB noise figure (NF) is achieved, with an RX gain of 40 dB, in-band (IB) IIP3 of -20 dBm and -3.5 dBm BldB at a 500 MHz offset, while occupying an active area of 1.62 mm2 and drawing a total power of 76.8 mW. at a frequency range of 22–31 GHz.
An Evaluation of Real-time Malware Detection in IoT Devices: Comparison of Machine Learning Algorithms with RapidMiner

In recent years, there has been a significant increase in malware attacks on IoT devices. As a result, there is a critical need to develop a robust malware detection model that can detect malware in real-time. This study explores different algorithms to identify the distinctions between various types of malware and develop a malware detection system based on botnets such as Mirai, Okiru, and Torii. We evaluate the performance of the malware detection system using RapidMiner and compare the results of different algorithms including Random Forest, Deep Learning, Naive Bayes, kNN, and Decision Tree. Our results show that the Random Forest algorithm outperforms the others and is the most effective at detecting malware in real-time.
Leveraging Wi-Fi 6 and MPTCP for Efficient and Reliable Real-Time Video Streaming in Safe Port Monitoring

Continuous and reliable unlicensed wireless con-nectivity solutions could play a fundamental role in the next generation of ports. To provide secure and reliable video moni-toring, Automated Rubber- Tyred Gantry cranes (AutoRTGs) rely on wired cable reel connections. By adopting 5G technology, smart ports can eliminate the need for fixed wired connections in favour of low-latency wireless communication, allowing effective communication and control. However, occasionally it is better to leverage complementary wireless technologies to carry resource-intensive traffic like large amounts of video data in uplink (UL). As a result, Wi-Fi is being considered as a potential solution. Wireless networks are widely utilized for their quick installation and simplicity. However, the presence of multiple unlicensed Wireless Access Networks could impact the wireless connection performance due to the possibility of channel interference. This paper studies how the utilization of a Multipath Trans-mission Control Protocol (MPTCP) wireless system can serve as an alternative to a fixed fiber cable reel. To minimize the latency, we propose the simultaneous use of two Wi-Fi 6 networks and MPTCP's redundant scheduler to send video streams from the crane to a remote control center (RCC) desk. We compare different wired and wireless topology alternatives to assess the most reliable network configuration when the AutoRTG is in operation. Thus, we evaluate each topology with dynamic quality of service (QoS) measurements. We find that duplicating packets with MPTCP over two Wi-Fi 6 networks allows for stable and reliable low-latency video streaming, even in instances where one of the networks experiences sudden high delay peaks. Finally, we discuss how the utilization of Wi-Fi and MPTCP can be a choice to support and complement 5G in situations with heavy uplink traffic.
Spectrum extraction from detector plane images for the medium-resolution spectrometer of the mid-Infrared Instrument on-board the James Webb Space Telescope

The Mid-Infrared Instrument (MIRI) on-board the James Webb Space Telescope (JWST) performs mediumresolution spectroscopy in the 5 to 28.5micron wavelength range. In this paper two algorithms are presented that will be used to extract 1D spectra from the 2D absolutely calibrated detector science frames acquired with the Medium-Resolution Spectrometer (MRS) of MIRI. The first spectral extraction algorithm performs standard aperture photometry on point and extended sources. The second algorithm, applicable only to point sources, uses the instrument point spread function (PSF) and the pixel signal variance as a weighting function, to extract the signal from the detector pixels in an optimized way. This "optimal" extraction is also optimal in the case of faint source observations. The two algorithms are tested on MIRI ground test data and compared. For point sources, the optimal extraction algorithm is found to be more reliable than the aperture extraction algorithm.
Clinically Prioritized Data Visualization in Remote Patient Monitoring

Understanding and integrating physiological data collected from wearable sensors in remote patient monitoring (RPM) is challenging. Data streams may be interrupted due to the sensor’s sensitivity, movement, and electromagnetic interference leading to inconsistent, missing, and inaccurate data. Existing approaches to summarize data flows into a single score such as the traditional Modified early warning score (MEWS) is limited. Data visualization approaches have the potential to address this challenge, but few studies have focused on visualization of RPM streams. The study presents a transformation of observed raw RPM physiological data into parameters identified as trust, frequency, slope, and trend. This facilitated visualization and enabled automated assessments of prioritized alerts. Experimental results have shown that the transformations led to the prioritization of clinically significant conditions, and improved visualization has the potential to better support clinical decisions compared with traditional MEWS.
A transmission design in dynamic heterogeneous V2V networks through multi-agent deep reinforcement learning

In highly dynamic and heterogeneous vehicular communication networks, it is challenging to efficiently utilize network resources and ensure demanding performance requirements of safety-related applications. This paper investigates machine-learning-assisted transmission design in a typical multi-user vehicle-to-vehicle (V2V) communication scenario. The transmission process proceeds sequentially along the discrete time steps, where several source nodes intend to deliver multiple different types of messages to their respective destinations within the same spectrum. Due to rapid movement of vehicles, real-time acquirement of channel knowledge and central coordination of all transmission actions are in general hard to realize. We consider applying multi-agent deep reinforcement learning (MADRL) to handle this issue. By transforming the transmission design problem into a stochastic game, a multi-agent proximal policy optimization (MAPPO) algorithm under a centralized training and decentralized execution framework is proposed such that each source decides its own transmission message type, power level, and data rate, based on local observations of the environment and feedback, to maximize its energy efficiency. Via simulations we show that our method achieves better performance over conventional methods.
Hybrid Radio Resource Management Based on Multi-Agent Reinforcement Learning

In this paper, we propose a novel hybrid grant-based and grant-free radio access scheme. We provide two multi-agent reinforcement learning algorithms to optimize a global network objective in terms of latency, reliability and network throughput: Multi-Agent Deep Q-Learning (MADQL) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG). In MADQL, each user (agent) learns its optimal action-value function, which is based only on its local observation, and performs an optimal opportunistic action using the shared spectrum. MADDPG involves the attached gNB function as a global observer (critic), which criticizes the action of each associated agent (actor) in the network. By leveraging centralised training and decentralised execution, we achieve a shared goal better than the first algorithm. Then, through a system level simulation where the full protocol stack is considered, we show the gain of our approach to efficiently manage radio resources and guarantee latency.
An EMI-Compliant and Automotive-Rated 48V to Point-of-Load Dickson-Based Hybrid Switched-Capacitor DC-DC Converter

With both data center power delivery and automotive powertrains tending towards a 48V distribution rail, high performance hybrid switched-capacitor (hybrid SC) converters have become an attractive power delivery solution in both spaces. However, automotive power systems present unique challenges in reliability and noise qualifications. This work investigates a regulating Dickson-based hybrid SC topology with low inherent electromagnetic interference (EMI) as well as mitigation techniques, such as an input filter and spread spectrum frequency modulation (SSFM). The proposed filter and modulation schemes enable this converter to meet automotive EMI standards. A hardware prototype combining a power stage and passive input filter is built to demonstrate the merit of hybrid SC topologies for use in 48V automotive systems.
A mm-sized implantable glucose sensor using a fluorescent hydrogel

This work proposes a millimeter-sized implantable glucose sensor using a fluorescent hydrogel based on boronic acids. The readout system is configured to provide an efficient wireless power transfer to the implantable sensor at 920-MHz frequency. The 675×959-µm2 die fabricated in 180-nm CMOS consists of a rectifier, a bandgap reference, two regulators, a trans-impedance amplifier, two voltage-controlled ring oscillators, and a digital control with the total power consumption of 150 µW. The die is assembled with a 2.2 × 2.5-mm2 antenna on PCB, an off-chip decoupling capacitor, an LED, and a photodiode. When the implantable sensor is surrounded by the fluorescent hydrogel and powered by the reader, the frequency of backscattered signals is encoded with the light intensity of the fluorescent glucose sensor, informing the glucose concentration. The miniaturization of the sensor minimizes the invasiveness, the potential discomfort, and immunity reaction against sensor, making it suitable for a long-term use after the implantation.
Adaptive Compensation Disturbance For Linear Systems With Input Delay*

An adaptive algorithm, compensating for unknown harmonic disturbance acting for linear objects under conditions of the unavailable state vector with a defined delay in the control channel is proposed. One of the features of the proposed method in comparison with other methods is that the perturbation signal is considered in the form of products of sinusoids. A new approach is proposed for estimating the frequencies of harmonic signal. It is assumed that all parameters of the multiharmonic disturbance (amplitude, frequency, and phase) are unknown. The task is completed in several steps. First, an observer is constructed based on a frequency estimation scheme. Secondly, stabilization of the ouput of object to zero is carried out using feedback based on the predictor. Examples are given that confirm the relevance of the proposed approach. Our main contribution is to propose a new scheme for compensating external disturbances for a linear plant and a new approach for estimating the frequencies of a multisinusoidal signal.
HYBRIDROID: Using Hierarchical Machine Learning Algorithms to Spot Malicious Applications on Linux-Powered Smart phones: Deep Survey

Since the previous two decades, mobile phones have played a significant role in our lives. Android is the most widely used operating system for mobile devices. The rapidly evolving environment surrounding Android has attracted hackers who want to produce malware. Applications from the Android Market and other websites can be downloaded and installed on Android devices. This gives hackers the chance to repackage harmful code and insert it into legal programmers. Numerous malware detection methods have been proposed, and several frameworks for their identification that make use of both static and dynamic investigative methods have been developed. Different machine learning techniques are used to classify apps based on behavior in a new fusion approach. This offers excellent efficiency and accuracy in malware detection by combining static and dynamic analysis. The results of the experiments demonstrated high precision and excellent memory and power efficiency.
Matrix Factorization with Landmarks for Spatial Data

Matrix factorization (MF) is widely adopted to learn from data, e.g., for data representation and recommendation as well as many database applications such as data imputation and repairing. While it works for numerical values in general, for spatial data, without considering the locality w.r. t. the spatial information, the learned features could vary in spatial distribution. Even if smoothness in terms of close neighbors could be considered in the objective function to leverage the spatial information, the learned features are still uncontrolled in locations, and thus do not help much in learning from the data that are geographically distant. Therefore, in this study, we propose to introduce landmarks to control the locations of learned features and make them geographically close to the data observations. The proposed SMFL, Spatial Matrix Factorization with Landmarks, benefits from landmarks in more accurate learned features, along with better interpretability, and reduced computation cost. Our major contributions include (1) introducing landmarks to guide the locations of learned features and enhance the performance as well as the interpretability of the MF model, (2) proposing the SMFL method that cooperates landmarks with NMF and spatial regularization, for better utilizing the spatial information, and (3) devising updating rules with landmarks and proving the convergence for the proposed method. Experiments on real-world datasets highlight the advance of our proposal in various applications.
Theoretical study of the Stoneley-Scholte wave at the interface between an ideal fluid and a viscoelastic solid

The propagation of the Stoneley-Scholte wave is analysed at the boundary between an ideal fluid and a viscoelastic solid. The theoretical study is developed using the approach of the inhomogeneous plane waves: the interface wave is thus regarded as a linear combination of one evanescent wave in the fluid and two heterogeneous waves in the solid. The main interest of the paper is the introduction of the viscoelastic solid, which is a medium characterized by physical attenuation, in the study of the propagation of the interface wave. Its mechanical viscosities are expressed in terms of acoustical attenuations, more easily determined by means of acoustical measurements. Existing mathematical tools are then used for solving the dispersion equation determined in this case. The study presented in the paper deals only with some aspects of the direct problem, that is the determination of the propagation properties of the Stoneley-Scholte wave, established for two different interfaces. A comparison of these characteristics in the presence and in the absence of attenuation, and a parametric study of the effect of attenuation, are included. The validity of the theoretical results are finally well demonstrated with some experimental ones, obtained with synthetic resins modelling viscoelastic solids.
Secure free space communication with vortex beams

Security is an increasingly important issue in modern communication. We propose a concept for secure detection of vortex beams in free space optics (FSO). The transmitted signal is composed of a Gaussian beam together with a low powered vortex beam. For each vortex mode, we select the detectors with the maximum received power for the particular signal, depending on the spatial distribution probability of the signal beam. Eavesdroppers have no way of knowing which patterns are relevant for the transmitted signal. We perform a numerical calculation for maximal signal detection and security. Applications include l free space communication.
Robust Control of Interleaved Boost Converter for Fuel Cell Systems

For the fuel cell system, the power converters can be applied widely due to the ability can solve the incompatibility of the bus voltage. To improve the power converter's ability to resist internal and external disturbance and accelerate dynamic response, a novel robust control method is implemented for the two-phase interleaved boost converter (TIBC), which contains the outer voltage regulation loop and the inner current tracking loop in this paper. First, the Model predictive control (MPC) is used to track reference current in the inner loop. The active disturbance rejection control (ADRC) can be applied to regulate the output voltage and generate reference current for the inner loop during the process of outer loop design. Then, the stability of the control strategy is verified by Routh-Hurwitz criteria. Finally, the effectiveness of the proposed control method is verified by simulation results, which indicates that the strong robustness and the fast dynamic response can be realized by the proposed method, when compared with the traditional dual-loop PI controller.
Deep Neural Networks for Halyomorpha Halys Detection

Pest detection and identification in a timely manner is a crucial step for precision agriculture. Halyomorpha Halys is a common pest whose negative effects are known in agricultural areas and on various crops. The present work implemented and studied four performant neural networks, VGG19_BN, EfficientNetB7, DenseNet161, and ResNet152 for the detection of these insects. Although the detection of these insects in the natural environment through automated means, excluding traps, is a challenge, the results obtained are promising.
Sabotage-proof Messenger Discovery for Protesters based on Brownian motion and Erasure Coding

Sabotage-proof message dissemination is critical for a successful protest. To successfully propagate the messages to the crowd, it is critical to develop a sabotage-proof protocol to discover the messengers for information relay. The protocol should be simple, efficient, and sabotage-proof. In this paper, we review the previous protocols and propose a new protocol that exploits the Brownian-motion of the protesters. The proposed distributed protocol is simple, but it is robust against sabotage. In every message propagation, the sender encodes the message with erasure coding into multiple packets, then the sender broadcasts one packet after another while the protesters are in Brownian-motion. The receivers that do not receive all packets but receive enough packets to decode the original message will be the potential messengers. Mathematical analysis and simulation verified the viability of the proposed protocol.
Reactive Company Control in Company Knowledge Graphs

The Company Control Problem consists in understanding who exerts decision power in companies. Central banks, financial intelligence units, and market regulators are all interested in this problem, which is crucial for their core goals. In the context where these actors operate, changes in company control call for immediate reactions.Yet, computing control relationships is a computationally expensive problem that involves traversing the entire shareholding structure and aggregating shares over multiple paths.In the context of the joint European banking supervision, the Bank of Italy will soon handle the shareholding graph of all European companies, which comprises hundreds of millions of entities (firms and individuals) and billions of edges and properties. This graph is highly volatile as the Bank continuously receives updates about shareholding relationships with unpredictable high frequency. This makes the straightforward bulk solution, where all the company control relationships are computed and materialized whenever a change occurs, unaffordable in practice.In this work, we present an incremental rule-based formalization of the problem, adopting the Vadalog fragment of the Datalog+/- families of languages. Our approach analyzes the specific change, singles out the portions of the graph that are affected by it, and selectively updates them. This allows one both to timely evaluate the impact of ownership variations on an extensive European-scale shareholding graph and to enable economists to perform the so-called "what-if analysis", i.e., simulation scenarios to proactively study the consequences of potential share acquisition operations, that currently are prohibitively time expensive. We provide an extensive experimental evaluation on very large company graphs, comparatively confirming the scalability of our technique in a real production setting.
Blind Spots: Identifying Exploitable Program Inputs

A blind spot is any input to a program that can be arbitrarily mutated without affecting the program's output. Blind spots can be used for steganography or to embed malware payloads. If blind spots overlap file format keywords, they indicate parsing bugs that can lead to exploitable differentials. For example, one could craft a document that renders one way in one viewer and a completely different way in another viewer. They have also been used to circumvent code signing in Android binaries, to coerce certificate authorities to misbehave, and to execute HTTP request smuggling and parameter pollution attacks. This paper formalizes the operational semantics of blind spots, leading to a technique based on dynamic information flow tracking that automatically detects blind spots. An efficient implementation is introduced and evaluated against a corpus of over a thousand diverse PDFs parsed through MµPDF11https://mupdf.com/, revealing exploitable bugs in the parser. All of the blind spot classifications are confirmed to be correct and the missed detection rate is no higher than 11 %. On average, at least 5 % of each PDF file is completely ignored by the parser. Our results show promise that this technique is an efficient automated means to detect exploitable parser bugs, over-permissiveness and differentials. Nothing in the technique is tied to PDF in general, so it can be immediately applied to other notoriously difficult-to-parse formats like ELF, X.509, and XML.