Miracle-dz/UniFAR-FTUs huggingface dataset:
RUSPATCH: Towards Timely and Effectively Patching Rust Applications

Despite the fact that Rust is designed to be a secure programming language for system programming, it is still vulnerable and exploitable due to its inclusion of an unsafe sub-language. However, existing studies on Rust security only focus on static detection or rectification of vulnerability, but ignore the problem of timely and effective rectifications of vulnerabilities dynamically. In this paper, to fill this gap, we present RUSPATCH, the first infrastructure to timely and effectively patch vulnerable Rust applications. RUSPATCH consists of two main phases: static partitioning and dynamic patching. In the static partitioning phase, RUSPATCH divides the candidate program into target code and patch candidates via a customized compiler. During the patching phase, RUSPATCH dynamically validates and applies the security patch once a vulnerability is detected. To realize the whole process, we tackled three technical challenges of language discrepancy, efficiency issues, and security threats. We have designed and implemented a software prototype for RUSPATCH, and have conducted extensive experiments to evaluate its effectiveness, performance, overhead, and usefulness. Experimental results demonstrated that RusPATCH is effective in patching off-the-shelf Rust applications including real-world Rust CVEs, and the extra overhead RusPATCH introduced is less than 3.28% and thus insignificant. Furthermore, RUSPATCH is easy to incorporate into existing Rust applications without any manual interventions.
Low Voltage and Low Power PLL with Current-Reuse Oscillator

In this paper, a low voltage and low power charge pump PLL based on current-reuse LC oscillator is designed. Different from the traditional differential LC VCO, the VCO in this design uses the cross-coupled pair of PMOS and NMOS to achieve the effect of Current-Reuse. The phase noise of the VCO is not affected by the second harmonic term of the common source node. In order to further reduce power consumption, the design uses 0.8V low voltage power supply and all the MOSFETs adopt a low threshold structure. The simulation results show that the power consumption of the whole circuit is 1.2mW and the phase noise at 1MHz frequency offset is -109dBc/Hz when the PLL output is 1.8GHz under the TSMC 180nm process and 0.8V supply voltage. The whole PLL can achieve integer frequency division of 1.68GHz $\sim 1.92$ GHz, and the locking time is less than 10us.
Pre-Transplantation Evaluation of Human Liver Using Polarization-Sensitive Optical Coherence Tomography

Liver transplantation is the most effective treatment of hepatic diseases but current evaluation of pre-transplantation faces challenges. Polarization-sensitive optical coherence tomography (PS-OCT) effectively provides a comprehensive and noninvasive evaluation of pre-transplantation quality throughout the entire human liver by quantitatively evaluating liver microstructures and tissue distributions.
Evaluation of requirements for phase-shift cavity ring-down spectroscopy biosensing using integrated microring resonators

Add-drop microring resonators (ADMRs) are promising devices for biosensing applications. Here we investigate the conditions required for successful use of the phase-shift cavity ring down spectroscopy (PS-CRDS) detection method for these devices. Parameters investigated include the radius, quality factor, and necessary PS-CRDS modulation rate. We also derive a relationship between quality factor and modulation rate for maximum sensitivity. We conclude that a Q factor of ~105 would be sufficient to permit a laser modulation frequency of less than 1 GHz, which would make this method practical for point-of-care applications.
Multi-Source Information Fusion with Multi-Criteria Evaluation

Dezert-Smarandache Theory (DSmT) can effectively model sensor information and offer combination rules for multi-source information fusion. However, fusion results obtained using DSmT are often unsatisfactory when a particular information source is unreliable. To improve the reliability of fusion results, a new multi-source information fusion with multi-criteria evaluation (FMCE) is proposed. Firstly, we evaluate the reliability of information source from two aspects: similarity and imprecision. Then, The results of the evaluation are used to calculate the appropriate weighting coefficient for each information source. Next, belief values are redistributed using the classical belief function discounting method. Finally, multiple sources of information are fused using the proportional conflict redistribution-5 (PCR5) rule, and we make decisions based on the minimum interval distance. Experiments on the UCI Smartphone dataset demonstrate the high accuracy of our FMCE method.
Removing Barriers for Participation of Small PV Systems in Balancing Energy Markets by Utilizing the Established Smart Meter Eco-System

With the number of PV systems installed in Germany rising again since 2018, the provision of system services is increasingly coming into focus. On sunny off-peak days, the use of PV systems to provide balancing power will be indispensable in the future power grid [1]. This paper is based on a joint project, which is funded by the Ministry of Science, Research and the Arts of Baden- Wiirttemberg, with the goal to develop a solution that will enable a large proportion of all photovoltaic (PV) systems to participate in the balancing power market [2]. In the context of the European harmonization of energy markets, the balancing energy market is therefore also undergoing change. In principle, PV plants have a very high potential for providing negative balancing power. Small PV systems with an active power limitation (peak power capping) of 70% can additionally provide considerable amounts of positive control power. Technically, cost-effective control of small-scale PV systems via the German smart meter infrastructure is a very attractive option based on the findings presented in this paper. Regulatory hurdles, such as prequalification or market conditions in the balancing energy market can also be overcome in the foreseeable future, in the view of the proj ect partners. However, stronger support at the political level is necessary for their timely elimination. An important step for a cost-efficient technical solution for the control of small PV plants results from the implementation obligation from the German federal law for the digitalization of the energy transition [3]. This conversion to smart metering represents an essential metrological basis for participation in the balancing energy market.As an additional benefit, the introduced intelligent metering system (iMSys) can also be used to control PV systems. The communication channel available for iMSys can essentially be shared here for controlling local plants without much additional effort. This paper proposes a concept that connects all systems involved to realize such an optimised plant controllability. For this purpose, a local control device (so-called CLS-gateway) connects the PV inverter with the remote control system of the balancing service provider (BSP). For communication, the secured channel provided by the iMSys is used, which is encrypted by the highly secured Smart Meter PKI. For the communication between the field device and the BSP control system, the MQTT protocol based on the publish-subscribe scheme is used [4]. The data models used are based on IEC 61850-7-4/420, but in contrast to the standard, they are mapped in JSON and not with MMS.
Paradigm Shift From Monolithic to Microservices

Microservices have been making waves among forward-thinking application development organizations. In the realm of software development, software architecture holds paramount importance because it serves as a guiding force to shape the entire life cycle of a software system. Software architecture is a foundation for complex digital components built upon a software system. Within this domain, two prevalent paradigms, monolithic and service-oriented architecture (SOA), stand distinct. While monolithic simplifies development using its integrated structure, SOA reduces complexity through modular services. However, both paradigms suffer severe scalability, development cycle, and flexibility challenges. Subsequently, microservice architecture as a modern paradigm emerges to overcome these challenges. This paper presents an in-depth analysis of the paradigm shift from monolithic to microservice architecture. It begins with exploring the monolithic and SOA conceptual landscape and their pros and cons. After that, we delve into the microservice platform, including its basic architecture and implementation stages. Furthermore, we provide the trend of the paradigm shift that highlights the recent developments in the field and identifies the research challenges associated with it. Thus, the paper brings multiple research dimensions for the researchers and lets the software and application development teams improve resilience and expedite their time to market.
Bioinspired Motion Information Guided Tracking Algorithm for Small Targets in Infrared Images

Researchers proposed the one-stream tracking framework to solve the problem that the features extracted by the two-stream tracking framework have limited target-background discriminability. OSTrack is a representative one-stream object tracking algorithm that unifies feature learning and relation modeling by bridging template-search image pairs with bidirectional information flows. Although OSTrack can improve target awareness, as the target is getting smaller and smaller, merely relying on the appearance information of the target is insufficient to track the target. In this paper, we propose an infrared small target tracking framework guided by bioinspired motion information, which introduces the motion information extracted by the magnocellular pathway. The spatial feature transformation is used to integrate the motion features with the appearance features. The proposed method achieved an accuracy rate of 84.70% and the success rate of 82.22% on the Anti-UAV dataset. The accuracy and success rate on small targets (targets less than 10×10 pixels) in the Anti-UAV data set are improved by 35.88% and 27.25% compared with the benchmark algorithm. The tracking performance of this algorithm is higher than mainstream tracking algorithms such as SiwnTrack, TransT, and MixFormer. The results indicate that introducing bioinspired motion information can effectively improve the tracking performance on small targets.
Single Person Identification Using Wi-Fi Signals

Over time, identification devices and applications have evolved. However, they still require people to perform certain actions to obtain their identification. Recently, Channel State Information (CSI) has emerged as a promising technology that resonates strongly in the evolution of identification systems. CSI allows obtaining information on the state of the transmission channel in Wi-Fi networks and offers a high granularity of data that can help the identification of a single person. In this work, we propose a new methodology for identifying a person using Wi-Fi CSI data. For this, we pre-process the received signal to eliminate unwanted propagation effects, such as noise and outliers, and also to smooth the signal. Then we use the signal amplitude of different Wi-Fi subcarriers as features for machine learning models to perform a person identification. Our proposal was validated with a CSI dataset collected from different participants performing diverse activities. The obtained results confirm the applicability of the proposed methodology as we obtained 92% average precision for identifying different people.
Separating Interferers from Multiple Users in Interference Aware Guessing Random Additive Noise Decoding Aided Macrosymbol

We differentiate between multiuser effects and interference in multiuser detection (MUD), and propose an approach that handles both of these. Users that are in a non-orthogonal multiuser access (NOMA) group employ symbol level multiple access channel codes, using the previously introduced method, guessing random additive noise decoding aided macrosymbol (GRAND-AM). Interferers outside the NOMA group, on the other hand, do not employ this symbol level multiple access coding technique. However, when the modulations and channel gains of the interferers are known at the receiver, we show how to modify the MUD in GRAND-AM in order to mitigate the effect of the interferers. We show that this interference aware GRAND-AM can greatly outperform MUD where the interference is assumed to be additive white Gaussian noise and subsumed into the signal and interference noise ratio.
Design of a High-Performance Power Supply Module with Subsection Compensation BGR and Capless LDO for Chip Applications

A high-performance power supply module for chip applications is presented in this paper using 180nm CMOS technology. The module includes a subsection compensation bandgap reference (BGR) and a capless low-dropout regulator (LDO) to address low temperature drift and low load adjustment (LDR) challenges. The temperature coefficient (TC) of the power supply module output voltage can be reduced using the subsection compensation technique. Additionally, the capless LDO with transient enhancement ensures proper functionality of the power supply module within the chip. Simulation results demonstrate that the proposed power supply module achieves a TC lower than 3 ppm/K, LDR lower than 0.002%, and overshot and undershot voltage lower than 400 $\mu$V, confirming its effectiveness in meeting low temperature drift and low load adjustment rate requirements.
What Type of Drug Would be Antiarrhythmic in Acute Myocardial Ischemia? Insights from Simulations

Acute myocardial ischemia (AMI) can lead to life-threatening arrhythmias. Hyperkalemia is a major contributing factor, increasing the risk of unidirectional block and re-entry. It also causes dynamic changes in the shape of the action potential, along with acidosis and anoxia. During the AMI process, this heterogeneity creates a vulnerable window (VW) in which reentry may arise due to an ectopic stimulus. Nevertheless, there is no drug with specific purpose to target the electrophysiological and arrhythmogenic outcomes of AMI. Based on our previous research, an ideal drug with calcium channel blocker properties could be effective. Hence, this study aims to assess the impact of calcium channel blockade on the efficacy of drugs in reducing VW and preventing arrhythmias during AMI using 3D computational simulations. A modified version of the O'Hara et al. computational model was used to include the effects of AMI and drugs. Simulations were carried out on a biventricular 3D anatomical model including a realistic shape of the ischemic and border zone, transmural heterogeneity, and anisotropy. The results suggest a biphasic behavior of calcium blockade on VWs: moderate-low or high blockade prevents arrhtyhmias, whereas low or medium-high blockade exacerbates them. Control Reentrant Probability Index (RPI) is X whereas a RPI of T, Q, Y, and Z is observed at calcium blocking percentages of 20%, 40%, 60%, and 80% respectively. In conclusion, this study demonstrates the feasibility of insilica experiments for classify and test the drug efficacy in reducing susceptibility to arrhythmias in AMI.
Lexical Acquisition from Audio-Visual Streams Using a Multimodal Recurrent State-Space Model

In this study, we present a novel approach to lexical acquisition using a mixture-of-product-of-experts (MoPoE) multimodal recurrent state-space model (MRSSM) with an assessment of its potential. The proposed MoPoE-MRSSM can aggregate multimodal information and perform cross-modal inference. Our approach is designed to provide a model of the process of lexical knowledge acquisition performed by infants in terms of word units, correspondences between words and objects, and transition probabilities between words by analyzing sequences of multimodal input streams comprising visual and auditory information. The proposed MoPoE-MRSSM was evaluated using a simple multimodal dataset that combined visual and speech information representing numbers from the MNIST dataset and TIDIGITS corpus. The results of an experimental evaluation of our method suggest that MoPoE-MRSSM can learn word units, i.e., the phoneme transition rules within words, and that the use of multimodal input improves its prediction performance. Because our method was not able to predict the probability of transitions between words sufficiently for practical applications, we also suggest some promising avenues for further research to explore this approach in more detail.
Enhancing Malware Detection Using “Genetic Markers” and Machine Learning

Despite the advanced techniques of malware detection using machine learning and deep learning, our community has been still suffering from new variants of malware in networks and systems. This paper proposes a new NLP-based malware detection method to generate a genetic marker by capturing the semantic behaviors of each malware family. The unique genetic markers aim to identify a specific malware family since each malware family has different characteristics of the distribution of opcodes. This paper evaluates the malware behavior based on opcodes for each family by using an NLP model and creates different templates to identify each malware family. Based on our experiments, our new approach achieved more than 99% detection rates and is fault-tolerant against various malware obfuscation techniques since the method captures the meaningful context of each malware sample.
Adaptive Dynamic Programming-Based Optimal Interaction Control of Modular Robot Manipulators Under Physical Human-Robot Interaction

In this paper, an adaptive dynamic programming (ADP)-based optimal interaction control scheme is proposed for modular robot manipulators (MRMs) under physical human-robot interaction (pHRI) tasks. Dynamic model of the MRM with multiple degrees of freedom is formulated through the use of joint torque feedback (JTF) technique. The radial basis function neural network (RBFNN) is utilized to carry out human motion intention recognition. Based on the ADP algorithm, the optimal control problem of pHRI task-oriented MRM systems is reconsidered as an optimal trajectory tracking problem. The optimal interaction control strategy for pHRI is obtainable by solving the Hamilton-Jacobin-Bellman (HJB) equation for the coupled human-modular robot interaction system using the ADP algorithm. The ultimately uniformly bounded (UUB) of the trajectory tracking error of the closed-loop MRM system under the pHRI task is proved by the Lyapunov theory. Finally, the effectiveness of the proposed method is verified by experimental results.
Blind Geolocation of RF-Signals with LEO Satellite Formations

This paper presents a hybrid geolocation approach specifically designed for passive geolocation of unidentified RF emitters. The proposed method leverages a sparse antenna array consisting of seven formation flying satellites, enabling geolocation without ambiguity or low resolution while avoiding the need to confine the solution space. Each satellite is equipped with a single-element antenna distributed over several kilometers, providing a highly aperture-efficient configuration and enabling precise Angle of Arrival (AOA) estimation. However, the inherent sparsity of the array introduces manifold grating lobes, leading to ambiguity in the AOA estimation process. To overcome this ambiguity, we employ Time Difference of Arrival/Frequency Difference of Arrival (TDOA/FDOA) geolocation estimation. The inter-satellite distance required for AOA estimation differs from that for TDOA/FDOA estimation. The latter imposes a maximum distance constraint among the AOA satellite formation. To establish the constraint condition, our approach involves a comprehensive analysis of the Cramer Lower Bound (CRLB) for both TDOA/FDOA in the first step and the confinement of the solution space for AOA geolocation estimation in the second step. By combining these three methods through a joint CRLB analysis, we gain valuable insights into the overall performance of the system. The integration of multiple geolocation techniques, strategic satellite configuration, and significant improvement in accuracy compared to existing methods constitute the key innovations of this research.
Towards Scalable Process Mining Pipelines

Over the past two decades, process mining has proven to be a valuable approach to gain insights into organizations' performance. The major sub-fields of discovery, conformance, and improvement have witnessed substantial development. Contributions have covered the spectrum of better algorithms, richer comparison metrics, and movement towards online analysis for process data. Mostly, these contributions were addressing process mining guidelines from the process mining manifesto. In this paper, we address the sixth guideline in the process mining manifesto. That is, process mining should be a continuous process. For this, we propose a pipelining approach that is: configurable, scalable, modular, and automated. We realize our proposal using Dask and evaluate it with different architectures, process discovery, and evaluation metrics.
P-doped bilayer graphene based photodetector with colloidal quantum dots absorber

Photodetector based on chemical free p-type doped graphene conduction channel and core-shell CdSe/ZnS type I quantum dots absorber was studied. Chanel conductivity modulation of 20% was observed under illumination. Responsivity of 0.72 A/W with 19ms response time and 1.4•108 Jones specific detectivity were achieved.
Network Clustering-Based Multi-Agent Reinforcement Learning for Large-Scale Traffic Signal Control

Reinforcement learning (RL) has proven successful in the field of traffic signal control (TSC) within urban traffic networks. Moreover, multi-agent RL (MARL) achieves large-scale TSC by distributing the global control to each local RL agent. However, as the scale of joint TSC continues to expand, MARL faces greater convergence challenges. In this paper, a method combining network clustering and MARL is proposed to address this issue. We apply the Normalized Cut (Ncut) algorithm to effectively partition a large-scale traffic network, and then adopt MADQN to complete TSC tasks for each subnetwork. Meanwhile, a novel reward function based on impedance has been adopted. The results demonstrate that our method has high control performance and could effectively alleviate congestion in large-scale network under peak-hour traffic conditions.
Effect of Stage Training for Long-Tailed Multi-Label Image Classification

In this study, we focus on the multi-stage training approach for training image classification models in the ICCV CVAMD 2023 Shared Task CXR-LT: Multi-Label LongTailed Classification on Chest X-Rays. In the proposed approach, the input image size and batch size are adjusted at each stage of the training process. In the first stage, we use a smaller input image size and a larger batch size for model training. Following that, we increase the image size and reduce the batch size in the second stage. A thorough search of the related literature did not yield validations of a similar approach for data with a long-tailed distribution. We successfully balance accelerated model training and performance by combining the proposed technique with various enhancements, such as oversampling, postprocessing using view positions, and ensemble methods, despite using a smaller model architecture and smaller input image size.