Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Flow Recording with InheritSketch on Programmable Switches

Several studies have been proposed to deploy the flow recording (i.e., flow size counting and sketching algorithms) on programmable switches for high-speed processing, helping network management tasks like scheduling. Although programmable switches provide a remarkable packet processing speed, they are of compact resources and follow a restrictive pipeline programming. To fit these limitations, current algorithms either sacrifice the recording accuracy or harm the switch throughput. In this paper, we propose InheritSketch for further improvement. InheritSketch utilizes a separation counting fashion, which is memory-efficient for compact switches. It accurately records the more valuable heavy hitters in the large key-value counters (i.e., the primary table), while only sketching non-heavy flows in the small sentinel table. With the recording ongoing, InheritSketch intelligently summarizes the historical recording experience as the basis for flow inheritance. That is, flows with the same IDs as the previous heavy hitters are regarded as new heavy hitters, being recorded in the primary table. To correct some incorrect inheritance, we also propose the flow rebellion, which promotes flows of large sizes but wrongly stored in the sentinel table to the primary table. InheritSketch is also helpful in applications like differentiated scheduling. We compare InheritSketch with six previous recording algorithms on three public traffic datasets, and prototype InheritSketch on a commodity P4 switch. The results demonstrate that InheritSketch reduces the recording errors by at most ∼7×, and that InheritSketch only consumes 10% of hardware resources on the switch.
Reduction of EMC Power Amplifier Intermodulation by Using Digital Signal Predistortion

The current EMC IEC 61000-4-3 standard includes multiple complex signals like multitone as a possible input signal for radiated immunity test. Nevertheless, due its nonlinear behavior, amplifiers can distort the multitone input signal to some degree, generating intermodulation products at the output which will result in unwanted and additional frequencies applied to the EUT that may affect the results of the test. This paper presents a predistortion method based on the Generalized Memory Polynomial model to compensate for the system nonlinearities. Simulations in MATLAB are performed considering a mathematical model of the physical EMC Sonoma 330 Broadband RF amplifier. The proposed predistortion method could successfully remove the intermodulation distortion products.
Synergistic Human-Computer/Robot Integration in Multifaceted Business Decision-Making Processes: An Overview

The rapid advancement of AI techniques has transformed decision-making by integrating AI and human intelligence within organizations. AI algorithms, combined with human talent, enhance decision-making by recognizing influential factors and adapting to complex situations. They have become essential different industries such as banking, healthcare, retail, supply chain, and transportation, enabling faster, objective, and cost-effective decisions. However, concerns about data privacy and algorithmic bias arise. This paper introduces the Throughput Model, a unique decision-making framework, integrating perception, information, judgment, and decision choice. It highlights the role of AI algorithms across industries and their alignment with the model under investigation. AI-powered algorithms enhance decision-making, increasing productivity and customer satisfaction. The paper suggests more research to evaluate the model's performance, address bias, explore human-AI collaboration, and optimize practical implementation.
Influence of Battery Aging on Converter Switching Device Degradation

In this work, a methodology to examine the influence of battery degradation on the converter semiconductor switching devices is presented. A PV-battery case study is used to demonstrate the methodology and show possible results. Initially, the results indicate a very limited influence of the battery on converter lifetime of 1 %. This is due to counteracting effects during battery degradation, which both decreases the power flow through the converter but increases the losses. A second simulation with constant power flow, isolating the effect of the reduced energy flow, showed an 9 % converter lifetime decrease using a degrading battery, demonstrating the underlying effect. The paper shows the battery model, converter model and case study results including battery degradation profiles, converter losses and reliability distributions.
Cooperative Adaptive Cruise Control for Vehicle Platoon Using a New Spacing Policy

In this paper, we propose a new spacing policy that integrates the conventional CS (Constant Spacing) and CTH (Constant Time Headway) into one for a vehicle platoon’s cooperative adaptive cruise control (CACC). To achieve excellent vehicle distance control performance and string stability simultaneously, the bidirectional vehicle-to-vehicle communication topology is adopted such that each vehicle receives the positions, velocities, and accelerations from both the adjacent predecessor and successor vehicles, by onboard sensors and/or wireless communication. The vehicle dynamics is a second-order nonlinear dynamic motion equation and a local robust controller is designed for each vehicle. The string stability of the platoon and the robust stability of the overall cooperative control system are analyzed theoretically. And finally, simulation studies are performed to support the theoretical insights.
Single Bean Region of Interest Auto-Detection in a Coffee Beans Image Towards Thorough Quality Inspection

Indonesia was declared the world's fourth-largest coffee bean producer in 2020 and yields consistently increase each year. Indonesia also has the Indonesian National Standard (SNI) 01-2907-2008, which specifies various defects with the defect scores of coffee beans that determine their quality. Common coffee bean defects listed in SNI 01-2907-2008 include black beans, broken black beans, partly black beans, and brown coffee beans. These defects degrade the quality and affect the market value. Previous studies have proposed computer vision systems as a non-invasive testing method for grading coffee beans based on trained images. However, obtaining the detail in a single bean image based on the region of interest could have been more comprehensively explained in those studies, making them less informative and requiring a more detailed conceptual breakdown. This study describes several coffee beans captured as images using a digital camera with a specific field of view, F/25 f-number, 1/60s exposure time, ISO400, and a lighting system reaching 12,000 lux. These images are processed based on the region of interest and centroid, resulting in single bean images with a resolution of 220 x 220 pixels ready for training. The samples used in this study are Robusta Lampung coffee beans, which are commonly found in Indonesia and exhibit various types of defects based on the criteria in SNI 01-2907-2008, such as black beans, broken black beans, partly black beans, and brown coffee beans.
Research on Endogenous Security Awareness Model In Information Systems

In the rapidly developing digital environment, the increasing complexity of network information systems and the diversity of data types pose a major challenge to the security of such systems. Traditional information security systems face limitations in perceiving granularity and obtaining a comprehensive understanding of the security state of the system, which makes it difficult to implement effective security defense. To address these challenges, this study presents an endogenous security awareness model (ESAM) based on the nervous system. This model links security component information to enhance information relevance in the information system and provides fine-grained component awareness. ESAM can identify attacks more effectively by capturing characteristics using many sensory neurons within the information system and generating new characteristics by correlating captured characteristics with states. The self-feedback threshold module incorporated in the ESAM allows for real-time threshold adjustment, which reduces the impact on the central control system and effectively defend against potential attacks. In this paper, we analyze the application of ESAM in DDOS attack scenarios. Our experimental results and discussions show that ESAM provides a promising solution for the limitations of traditional information security systems and a more comprehensive and effective method for the security of network information systems. This model makes a valuable contribution to the field of endogenous security by enhancing information relevance and enabling fine-grained component awareness.
The Method for Reducing Memory Costs for Messages Processing

The paper considers the problem of processing messages of limited size. An effective means of ensuring the authenticity and confidentiality of such data is the use of the clipper block chaining encoding mode. It is shown that the organization of intermediate result storage requires the implementation in the memory of the calculator of blocks responsible for the formation of a tree structure containing both messages from the target source and messages from other sources functioning as part of a complex distributed system. To do this the incoming messages are excluded from processing based on the contents of an additional field that stores the encoded sequence number of the message in the chain. This reduces the number of branches in the generated and processed message tree structure. The effect of this is to reduce the cost of RAM of the computer performing decoding and forming message chains from the target source. It is shown how the implementation of the method of processing message authentication codes reduces the number of rows and columns of internal memory that store an element of a tree structure. The analysis of the results of the conducted studies showed that the result of using the processing method is a decrease of the cost of register memory for storing intermediate results by 5-10 times compared with the results of the previous studies.
Lightweight Secure Identity Authentication Scheme for Restricted IoT Devices

With the wide application of IoT in many fields, IoT security has become the main reason to hinder the development of IoT. Two-way authentication of IoT devices is a basic requirement to ensure the communication security of IoT devices, however, for IoT devices with limited computation, storage and energy, the security schemes in traditional Internet cannot be applied to the restricted IoT devices. To solve these problems, this paper designs a lightweight secure authentication scheme for constrained IoT devices. The scheme uses pre-shared key (PSK) combined with temporary key generated by temporary elliptic curve (ECDHE) to generate a session key for the session, and completes the challenge answer by the session key to achieve two-way authentication of the device. The scheme is implemented by ActiveMQ and FuseSource MQTT, and experiments are conducted to compare different metrics with ECDHE-RSA cipher suite and AugMQTT authentication scheme in TLS protocol, respectively. The experimental results show that the authentication scheme designed in this paper makes up for the forward security defect in the AugMQTT authentication scheme, and the scheme is more secure; compared with the ECDHE-RSA authentication method, the execution time and CPU usage are reduced by 20% and the bandwidth usage is reduced by nearly 50%, and the scheme is more lightweight and suitable for resource-constrained IoT devices.
Traffic Prediction Method for Time Series Networks Based on ARIMA-LSTM Model

The increasing scale of network access in recent years requires an advanced analysis of future network data traffic(NDT) trends to anticipate various network scenarios. However, current prediction methods are limited in accuracy due to the overly complex components of network traffic on the time scale. This paper proposes a time series network traffic prediction(NTP) method based on the ARIMA-LSTM model. The method uses the ARIMA to extract the linear components of the web traffic data and then uses the LSTM to extract the nonlinear components further. It will achieve effective prediction of complex time series. The article validates the effectiveness of the method experimentally on two publicly available datasets. All experimental results show that the ARIMA-LSTM method has satisfactory prediction performance and outperforms several other state-of-the-art works. In sum, this NTP method points to a new orientation for NDT monitoring and analysis.
An Online Randomized Field Experiment on the Importance of Privacy Education, Training, and Awareness (PETA)

This study examines the effect of privacy awareness on decisions to disclose personal information related to various security measures at the individual level. Explaining and predicting why individuals decide to disclose or withhold security-related preferences and behaviors (e.g., use of password, passphrase, 2-factor authentication, frequency of updating security software and operating systems, etc.) is a critical research and practice endeavor because such personal information is highly sensitive and can be detrimental when it lands in the hands of social engineers. We conducted a randomized field experiment in which privacy awareness was manipulated via a simple privacy nudge and disclosure behavior was measured by asking subjects to respond to a series of personal questions related to security preferences and behaviors. The experiment was conducted online among Saudi Arabian subjects who signed up for a 2-hour Security Education, Training, and Awareness (SETA) workshop during which they learned about various security protection measures. We used a basic between-subjects experimental design and conducted the experiment in three waves of data collection (N = 2,668): wave 1 was conducted immediately prior to the SETA workshop; wave 2 was conducted immediately after the SETA workshop; and wave 3 was conducted two weeks after the SETA workshop. Our aim is to test whether the privacy awareness nudge makes a difference in terms of whether individuals disclose information related to their security preferences and behaviors. The findings show that our participants overall disclosed most information asked for in waves 1, 2, and 3. This is an alarming observation regardless of the context of our experiment. However, participants who received the privacy nudge disclosed less information than those who did not receive the privacy nudge in waves 1, 2, and 3. These findings suggest that Privacy Education, Training, and Awareness (PETA) is necessary to supplement existing SETA programs. We discuss our contribution to the cybersecurity literature and practice and provide a short conclusion on how our experiment applies to what we coin the ‘general relativity of privacy’.
Superposition Construction of Globally-Coupled LDPC Codes for MIMO Communication

This paper presents a globally-coupled low-density parity-check (GC-LDPC) coding scheme with high throughput for multiple-input and multiple-output transmission. To design rate-compatible length-scalable GC-LDPC codes, a novel superposition construction of GC-LDPC codes is proposed. To reduce the complexity of the two-phase decoding (TPD) method, we propose a modified TPD method based on partial check matrices. Numerical results show that our coding scheme can divide the decoding of a large code into 4 and 5 parallel decodings of short codes and increase throughput by 4 and 5 times at the frame error rate of $10^{-5}$ for code rates of 0.49 and 0.66, respectively. Besides, the modified TPD method reduces the global decoding complexity to almost 1/4 and 1/5 of the TPD method at the frame error rate of $10^{-5}$ for code rates of 0.49 and 0.66, respectively.
Design of Soft Pneumatic Actuator in Hand Rehabilitation Robot with FEM-Based Modeling

One motor function stroke patients often struggle with is hand movement, specifically in the hands and fingers. Routine rehabilitation is necessary for the patient to regain their hand-motor skills, which can be achieved using rehabilitation robots. However, traditional rehabilitation robots use rigid electro-mechanical approaches that lack personalization. Soft robotics with an electro-pneumatic approach is a more effective solution, utilizing soft pneumatic actuators (SPA) that are customizable and fabricated with 3D printing, such as pneumatic network structure (Pneu-Net). However, manufacturing every designed Pneu-Net model will require a lot of printing costs and testing time. This study used finite element method (FEM) modeling to streamline Pneu-Net design, fabrication, and testing. The SPA material was modeled using a hyperelastic modeling approach. The bending angle characteristics of the SPA model were simulated for specific pressure inputs using Abaqus CAE software. The results show that the hyperelastic model accurately represents the SPA's performance in real-world conditions with average errors for three sets of material parameters being 7.93%, 6.65% and 7.20%.
Approaches to teaching embedded development

There is an ever-increasing demand for the embedded systems skillset in both research and industry projects. Aside from requiring a high level of understanding in Operating Systems and Computer Architectures, one also needs to master low-level programming, compilers, build systems and debugging skills. In this paper, we present our take on organizing an ARM development workshop indended for university students pursuing Bachelor’s and Master’s degrees. Our work aims to instill fundamental knowledge of embedded systems architecture and low-level software development on ARM Cortex-A platforms. Our curriculum encompasses lectures and practical lab sessions focusing on building the firmware, bootloaders and Linux distributions from scratch for real-life hardware. This paper asserts three key contributions: first, it proposes a methodology for teaching systems engineering, with specific emphasis on the ARMv8 architecture. Second, it delves into our challenges encountered during the workshop, exploring potential solutions and alternatives. We also analyze the feedback from the participants, which gives insights on what went well, what didn’t, where the main difficulties lie and suggestions for overcoming them in future iterations. Lastly, the paper highlights our commitment to education by sharing workshop materials on the university’s Open Courseware platform.
Modeling of Operator Poses in an Automated Control System for a Collaborative Robotic Process

One of the most effective ways to ensure the competitiveness of industrial enterprises is the integrated automation of technological processes. Technological processes with a high degree of non-determinism of the internal and external environment can be automated using collaborative systems. However, at the moment, the potential of collaborative robotic processes is not fully realized. I In particular, effective safety issues of the cobot's control algorithms have not been resolved in the event of an emergency situation that can lead to negative consequences in relation to other objects of the working area - another robot, operator, product, equipment. It becomes obvious that there are simulation models of the collaborative process, including the digital twin of the operator in modeling and predicting the kinematics of his movement. The presence of a forecast can prevent the risk of an unsafe situation in the process of technology execution or in testing mode in time. In the course of the research, a model of the kinematics of the operator's movement is presented (integrable into the general model of the technological process), based on standard poses and adequate in terms of motion dynamics. The model serves to visualize the set poses of a person, allows to track the admissibility and effectiveness of the execution of the robot's control optimization algorithms, and adequately reflects the dynamics of the operator's movements.
Geometric Calibration of Spaceborne Bistatic SAR LT-1 for Generation of High-Accuracy DEM

Spaceborne bistatic Synthetic Aperture Radar (SAR) is a feasible system for generating a Digital Elevation Model (DEM) through the synergy of multiple SARs. The error of systematic delay caused by the internal device performance and the external environment reduces the height accuracy of DEM. Thus, geometric calibration that corrects the systematic delay error is necessary. Range-Doppler (RD) model is an effective calibration method. However, the working principle of the bistatic SAR is different from the traditional monostatic SAR, and the calibration model should be modified. Therefore, we propose the geometric calibration method for the bistatic system in this paper. The method is applied to LuTan-l (LT-1), and the study is conducted in the Hami Gobi in Xinjiang, China. We verify the method by selecting one pair of interferometric images of LT-1 with corner reflectors. The experimental result shows that the error of systematic delay leads to 26.09 m height error. In addition, the height accuracy of DEM is 1.41 m after geometric calibration and achieves the height accuracy requirement of 1:50000 scale topographic map.
Learning to Decode Linear Block Codes using Adaptive Gradient-Descent Bit-Flipping

In this paper we propose a generalization of the recently published adaptive diversity gradient-descent bit flipping (AD-GDBF) decoder, named generalized AD-GDBF (gAD-GDBF) decoder. While the original AD-GDBF decoder was designed for the binary symmetric channel and used mostly to decode regular low-density parity-check codes, the gAD-GDBF algorithm incorporates several improvements which makes it eligible for the additive white Gaussian channel and decoding of arbitrary linear block code. The gAD-GDBF decoder uses the genetic algorithm to optimize a set of learnable parameters, for a targeted linear block code. The effectiveness of the proposed method is verified on short Bose-Chaudhuri-Hocquenghem (BCH) codes, where it was shown that for the same number of decoding iterations the gAD-GDBF decoder outperforms the belief-propagation decoder in terms of bit error rate and at the same time reduces the decoding complexity significantly.
Research on Active Disturbances Rejection Control Strategy of Water-Lubricated Bearing Lubrication System

Lubrication system for water-lubricated bearing to provide a stable water supply pressure and flow, its stability is vital to the formation of water film and stable operation of the bearing; however, the general water-lubricated bearing lubrication system also has a large time lag, multiple coupling, weak anti-interference ability and other deficiencies. In view of the above problems, this paper proposes a self-turbulence control strategy, i.e., the active disturbances rejection controller (ADRC) is used to control the lubrication system water supply pressure and then regulate the system water supply flow. The introduction of a water-lubricated bearing and lubrication system control strategy, coupled with the design of an expansion state observer (ESO) to estimate system and external disturbance based on the mapping relationship between solenoid regulating valve opening and water supply pressure, is the first step. The estimated total disturbance is used to construct an active disturbances rejection controller; then, the active disturbances rejection control algorithm is studied and a mathematical and simulation model is established; finally, a simulation test is conducted to compare with the PID control effect. The results demonstrate that the control strategy and system have been successful in managing sudden pressure alterations, thus enhancing the lubrication system's accuracy, response speed, and robustness. The step response time of the lubrication system is improved by 25.29% relative to the PID control method, and the overshoot is decreased by 4.47%.