Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Data Poisoning in Split Learning Using Intraclass- Distance Inflated Loss

Data poisoning attacks are a growing threat to the security of privacy-preserving machine learning. We investigate the impact of a static label flipping attack, an instance of data poisoning attacks, on Split Learning (SL). To counteract this threat, we introduce a novel detection method. Our approach leverages an autoencoder in the split layer of the system and combines client loss scores and the distance of data points from the same class in the latent space of the autoencoder. In doing so, we gain insight into client behavior and data quality, allowing a comprehensive defense strategy. Through experiments, we validate the effectiveness of our detection mechanism to accurately identify malicious clients. By integrating our detection mechanism during training, we successfully restore the system's baseline Area Under the Receiver Operating Characteristic curve (AUROC) performance while under attack with a negligible decrease (down to 0.001 in AUROC) by excluding malicious clients from the collaborative training. Our research underscores the importance of tackling data poisoning attacks within SL systems, thereby enhancing their overall security and robustness.
PWM converter with low stresses and zero capacitive turn-on losses

A buck converter operating at constant switching frequency, whose active switches and recovery diode commutate at zero-voltage-switching (ZVS), with zero capacitive turn-on losses, is proposed. By using the parasitic capacitances of the switches as resonant capacitors, multiresonance is created. The resonant stage takes place only after the resonant inductor has been discharged, thus avoiding a resonant current peak; the devices are subjected to the same stresses as their counterparts in conventional hard-switching converters. A high efficiency is obtained.
Social Learning methods in board game agents

This paper discusses the effects of social learning in training of game playing agents. The training of agents in a social context instead of a self-play environment is investigated. Agents that use the reinforcement learning algorithms are trained in social settings. This mimics the way in which players of board games such as scrabble and chess mentor each other in their clubs. A round robin tournament and a modified Swiss tournament setting are used for the training. The agents trained using social settings are compared to self play agents and results indicate that more robust agents emerge from the social training setting. Higher state space games can benefit from such settings as diverse set of agents will have multiple strategies that increase the chances of obtaining more experienced players at the end of training. The social learning trained agents exhibit better playing experience than self play agents. The modified Swiss playing style spawns a larger number of better playing agents as the population size increases.
Multiple paternity in a salamander with socially monogamous behaviour

In the majority of birds and mammals, social monogamy is not congruent with genetic monogamy. No research to date has compared social and genetic monogamy in amphibians. We analysed paternity in clutches of red‐backed salamanders (Plethodon cinereus), a species in which social monogamy has been demonstrated in the laboratory, and 28% of individuals in the forest are found in male‐female pairs in the noncourtship season. We collected 16 clutches of eggs of P. cinereus in the southern Appalachian Mountains of Virginia and collected tail clippings from attending mothers. We genotyped embryos and adults at five microsatellite loci in order to analyse paternity of clutches. Most clutches (84.6%) had multiple sires, with two to three sires per clutch. In this study, 25% of clutches had males in addition to females attending eggs. None of the mothers of these clutches were genetically monogamous. All attending males sired some of the offspring in the clutch that they attended (between 9% and 50%) but never sired a majority in that clutch. We conclude that, at least in this population, social monogamy in P. cinereus is not concomitant with genetic monogamy.
Quasi-static scheduling for safe futures

Migrating sequential programs to effectively utilize next generation multicore architectures is a key challenge facing application developers and implementors. Languages like Java that support complex control- and dataflow abstractions confound classical automatic parallelization techniques. On the other hand, introducing multithreading and concurrency control explicitly into programs can impose a high conceptual burden on the programmer, and may entail a significant rewrite of the original program. In this paper, we consider a new technique to address this issue. Our approach makes use of futures, a simple annotation that introduces asynchronous concurrency into Java programs, but provides no concurrency control. To ensure concurrent execution does not yield behavior inconsistent with sequential execution (i.e., execution yielded by erasing all futures), we present a new interprocedural summary-based dataflow analysis. The analysis inserts lightweight barriers that block and resume threads executing futures if a dependency violation may ensue. There are no constraints on how threads execute other than those imposed by these barriers. Our experimental results indicate futures can be leveraged to transparently ensure safety and profitably exploit parallelism; in contrast to earlier efforts, our technique is completely portable, and requires no modifications to the underlying JVM.
Exploring Neural Networks for Time Series Classification in Communication Networks

paper explores the utility of diverse neural community architectures to time series category issues in verbal exchange networks. The authors first compare diverse architectures primarily based on their simple idea of characteristics for categories in conversation networks. Various methods were investigated to acquire better consequences, including fitting, batch normalization, dropout, and switch learning. The consequences acquired from the experiments confirmed that the distinct architectures supply better performance compared to the conventional strategies in the time collection type. The authors also discussed several upgrades incrementally from the viewpoint of communication networks while offering a perception of their workings. The research outcomes can be a doubtlessly valuable device to help communications networks improve their performance and safety. Exploring Neural Networks for the Time series category in communication Networks is the latest research topic focusing on growing gadget learning fashions for time-structured facts in communication networks. The goal of this study is to benefit a higher knowledge of the shape of the time collection and allow green classification of the records. Primarily, neural networks, customarily used for sample popularity, can be implemented to deal with time series class obligations in communication networks. By using training neural networks on time series information, it is viable to examine patterns and find correlations among them. The resulting fashions are then used to classify unseen records factors and discover anomalies within the facts.
A Framework to Construct Financial Causality Knowledge Graph from Text

Causality analysis holds a prominent role in finance, and the presentation of causality could offer valuable insights for risk mitigation, investment decisions, and portfolio optimization. Recent research has extensively investigated the identification of causality from text, yet there is still a significant deficiency in providing a comprehensive causality presentation from those textual discoveries. In this paper, we present an end-to-end framework to automatically construct Financial Causality Knowledge Graph (FinCaKG) from text, which allows us to visualize the captured causality from a holistic perspective. This framework involves three distinct tasks, including causality sentence detection, cause-effect span identification, and causal dependency representation. To examine the adaptability of FinCaKG framework, we generate, compare, and analyze the distinct FinCaKGs created from different corpus. The results show that this framework has the capability to not only capture the confidential causality but also represent them in a highly detailed manner in the resulting knowledge graph - FinCaKG. We perform a comparative study with ConceptNet, revealing the notable contributions of FinCaKGs in terms of domain coverage and the density of its causal knowledge.
Ensemble Method in Parkinson's Disease Classification via EEG signals

One of the most prevalent neurological diseases around the world is Parkinson's disease (PD). It affects a lot to people's lifestyles because of many symptoms. Electroencephalogram (EEG), which is one of the hot topics that attracts researchers, can be used to diagnose PD. In our research, three public datasets are used to classify PD named UNM, Turku, and Iowa. The EEG signals are collected while participants are in the resting state. Diagnosing PD model in our study is designed consisting of three main parts: pre-processing, feature extraction, and classification with machine learning algorithms. The Discrete Wavelet Transform (DWT) was used to extract seven features to five bands (delta, theta, alpha, beta, and gamma). Then, the feature matrix was applied to classifiers using the Ensemble method and Artificial Neural Network (ANN). To evaluate the performance of the model, accuracy, sensitivity, and specificity was used. With the eye-opened condition, the Iowa dataset achieved an accuracy of 88.50% and 88.46% when using ANN and Ensemble methods, respectively. The sensitivity and specificity of the two classifiers are both good, higher than 80% as well as no bias for one label. The UNM and Turku in closed and open eye conditions also had better results than state-of-the-art, with 98.54% and 91.24% accuracy, respectively, when using the Ensemble method.
Research Progress of Bridging Digital Divide for Older Adults through Information Technology Training—A Bibliometric Analysis and Visualization Based on CiteSpace

In global aging, the application of information technology (IT) shows its potential to improve the quality of life of older adults and promote active aging. However, there is still an unbridgeable digital divide among citizens that prevents them from benefiting from IT. It is necessary to maximize the realization of active aging, bridge the digital divide, and provide IT training for older adults. Using a bibliometric analysis method and CiteSpace visualization analysis, we investigated the development status and trend of IT training research for older adults. Few research achievements were observed in this field, although the publication level become better. The cooperation of relevant research institutions is loose, and research topics are relatively concentrated on three aspects: physical health, mental health, and evaluation. In general, there is room for development, and researchers need to cooperate widely on research.
An Adaptive Update Strategy for KCF

Great advance has been achieved to improve the CF-based trackers, most of them ignore to improve update scheme. The update scheme plays an important role in tracking, which helps the tracker adapt the change of target. This paper presents an adaptive update strategy by introducing the peak to sidelobe ratio (PSR) for kernelized correlation filter (KCF) tracker. It helps the KCF based trackers adapt to appearance changes and achieves excellent tracking performance under complex changes. The extensive evaluations on OTB2015 benchmark show that our tracker significantly enhances the tracking performance.
Using Polynomial Wigner–Ville Distribution for Velocity Estimation in Remote Toll Applications

This letter presents the use of polynomial Wigner-Ville distribution (PWVD) for accurate velocity estimation as used in remote road traffic management applications. In such applications based on inverse synthetic aperture radar, velocity estimation is central to obtain a suitable level of performance of the signal processing. Moreover, the precision of this velocity estimation is crucial in order to achieve the best detection and estimation of the gauge of the vehicles through the use of radar images. Hence, the PWVD is applied as an instantaneous frequency estimator used in this traffic surveillance application.
Proposing Appropriate SMS Spam Detection Approaches for Variations of the Vietnamese Language

This paper introduces suitable methods for detecting SMS spam in various forms of the Vietnamese language. The researchers conducted experiments using five algorithms: SVM, Naive Bayes, Random Forests, CNN, and LSTM, across three different Vietnamese datasets. The results indicate that LSTM and CNN, complemented by a transformer learning model called PhoBert, outperformed traditional machine learning models. Specifically, the LSTM model achieved the highest accuracy of 97.77% when applied to the Vietnamese dataset with full diacritics. Similarly, the CNN model and PhoBert model achieved the highest accuracy of 95.56% when working with the Vietnamese dataset without diacritics.
Application of Synthetic Geomagnetic Storms on Transformer Thermal Assessment

The lack of extensive data on severe geomagnetic storms has necessitated the need to generate synthetic time-series electric field, particularly to assess and predict the im-pact of such extreme storms on the resilience of the electric power grid. Due to geomagnetic disturbances, geomagnetically induced currents (GIC) can lead to half-cycle saturation of the transformers, placing the equipment at the potential risk of experiencing increased hotspot heating. This paper describes a methodology for generating extreme synthetic geomagnetic storms through an iterative process of temporally and spatially varying the fragments of the NERC benchmark event by scaling the time duration, magnitude, and direction of the storm. In addition, the results of the thermal sensitivity of the transformers to the assumed quasi-de electric fields simulated on a large-scale synthetic electric grid are presented. Further engineering assessments may utilize the computed extreme GIC scenarios.
Robust nanogenerators based on graft copolymers via control of dielectrics for remarkable output power enhancement

Robust nanogenerator based on poly(tert-butyl acrylate)–grafted PVDF copolymers via dielectric constant control is demonstrated. A robust nanogenerator based on poly(tert-butyl acrylate) (PtBA)–grafted polyvinylidene difluoride (PVDF) copolymers via dielectric constant control through an atom-transfer radical polymerization technique, which can markedly increase the output power, is demonstrated. The copolymer is mainly composed of α phases with enhanced dipole moments due to the π-bonding and polar characteristics of the ester functional groups in the PtBA, resulting in the increase of dielectric constant values by approximately twice, supported by Kelvin probe force microscopy measurements. This increase in the dielectric constant significantly increased the density of the charges that can be accumulated on the copolymer during physical contact. The nanogenerator generates output signals of 105 V and 25 μA/cm2, a 20-fold enhancement in output power, compared to pristine PVDF–based nanogenerator after tuning the surface potential using a poling method. The markedly enhanced output performance is quite stable and reliable in harsh mechanical environments due to the high flexibility of the films. On the basis of these results, a much faster charging characteristic is demonstrated in this study.
Dedup Est Machina: Memory Deduplication as an Advanced Exploitation Vector

Memory deduplication, a well-known technique to reduce the memory footprint across virtual machines, is now also a default-on feature inside the Windows 8.1 and Windows 10 operating systems. Deduplication maps multiple identical copies of a physical page onto a single shared copy with copy-on-write semantics. As a result, a write to such a shared page triggers a page fault and is thus measurably slower than a write to a normal page. Prior work has shown that an attacker able to craft pages on the target system can use this timing difference as a simple single-bit side channel to discover that certain pages exist in the system. In this paper, we demonstrate that the deduplication side channel is much more powerful than previously assumed, potentially providing an attacker with a weird machine to read arbitrary data in the system. We first show that an attacker controlling the alignment and reuse of data in memory is able to perform byte-by-byte disclosure of sensitive data (such as randomized 64 bit pointers). Next, even without control over data alignment or reuse, we show that an attacker can still disclose high-entropy randomized pointers using a birthday attack. To show these primitives are practical, we present an end-to-end JavaScript-based attack against the new Microsoft Edge browser, in absence of software bugs and with all defenses turned on. Our attack combines our deduplication-based primitives with a reliable Rowhammer exploit to gain arbitrary memory read and write access in the browser. We conclude by extending our JavaScript-based attack to cross-process system-wide exploitation (using the popular nginx web server as an example) and discussing mitigation strategies.
Q-Delegation: VNF Load Balancing Through Reinforcement Learning-Based Packet Delegation

While Network Function Virtualization (NFV) and Service Function Chaining (SFC) offer unprecedented capabilities to network operators, there are still performance concerns that need to be addressed, especially with the advent of network slicing requiring automated and efficient resource allocation. In this context, sharing the available networking and computational resources is an appealing option to minimize under-utilization, but this can lead to over-subscription of the available resources under heavy traffic loads. To overcome this, distributed load balancing mechanisms, such as packet delegation, can be utilized to move the decision-making further from centralized orchestrators and closer to the Virtual Network Functions (VNF) themselves, allowing them to quickly redirect incoming traffic to other servers when they are overloaded. However, the effectiveness of packet delegation can vary considerably depending on the selection of the target VNF that receives the delegated packets. As such, in this paper, the Q-Delegation algorithm is proposed, which treats this as a non-stationary multi-agent Reinforcement Learning (RL) problem, providing an adaptive way of selecting appropriate delegation targets based on the VNF's current queueing state through Q- Learning. Two variations of the Q-Delegation algorithm are proposed, either allowing an RL agent to act once a VNF's queue has exceeded a static threshold or enabling it to freely learn how to reroute packets to other VNFs regardless of their queue size. Both variations are experimentally evaluated and compared to other baseline algorithms under diverse topologies and traffic rates in a simulation setting, indicating that in most cases both variations of Q- Delegation can be effective at delegating packets and outperforming the baselines, though their effectiveness can vary based on how evenly the traffic is initially distributed.
CNN Model for the Small Data in Vehicle Miniature Classification

Convolutional Neural Network (CNN) becomes the robust deep learning model for image classification. It has many configurations applied to achieve the best performance. A high number of data is needed to build a good model of CNN. It makes small data cannot support optimally to build a robust model. In fact, certain cases have limited sources of data. This study aimed to build CNN robust model using small dataset. In this research, several models were proposed and vehicle miniature dataset was selected. Configurations for optimizer, input size, batch size, and pooling were also done to obtain the expected model. Data augmentation became an experiment for comparing the model performance. Based on proposed model, the results show that model obtain 84.87% of accuracy using data augmentation and more than equal to 98.69% of accuracy without data augmentation. Moreover, this study is also able to increase accuracy score when compared to previous research.
Design and Development of AUDI'S AUV as an Underwater Robot Prototype

AUDI'S AUV is a robot technology that is very necessary for underwater exploration. AUDI'S AUV research includes various stages starting from body manufacturing design and hardware development, to software development to control and operate the AUV autonomously. AUDI'S has the main mission to maneuver autonomously underwater along a predetermined path and an additional mission in the form of the ability to maneuver past obstacles and then retrieve metal objects at the bottom of the pool using ferrite magnets. AUDI'S AUV uses various sensors such as pressure sensors, IMU (Inertial Measurement Unit), Ultrasonic Rangefinder, and cameras. All sensor data is processed using data fusion via the EKF (Extended Kalman Filter) method. AUV control uses a Python language programming algorithm and uses the Drone kit and link libraries which are integrated with OpenCV image processing so that it can maneuver underwater autonomously. This robot research obtained the results of the deepest dive, namely 2 meters in a swimming pool and displayed the results of observations in the water in the form of video which was displayed directly on the monitor. This underwater robot still requires some development so that it can be used in the sea.
A Finite Blocklength Coding Scheme for the AR(1) Gaussian Channel with AWGN Feedback Channel

The first-order autoregressive (AR(1)) Gaussian channel, which is useful in establishing conceptually insightful results on fading channels, is extensively studied in the literature. In this paper, the AR(1) Gaussian channel with additive white Gaussian noise (AWGN) feedback channel is investigated. A finite blocklength (FBL) coding scheme is proposed for this model, which combines a Schalkwijk-Kailath (SK) type scheme for the Gaussian relay channel with noiseless feedback and an iterative feedback scheme for the AWGN channel with noisy feedback. An achievable rate of our proposed scheme is derived for given coding blocklength and decoding error probability. Numerical results show that when the signal-to-noise ratio of the feedback channel is significantly larger than that of the forward channel, our proposed scheme approaches the channel capacity of the AR(1) Gaussian channel with noiseless feedback at extremely short blocklength.