Miracle-dz/UniFAR-FTUs huggingface dataset:
Program Balancing in Compilation for Buffered Hybrid Dataflow Processors

In traditional von Neumann processors, the central register file is an inherent limiting factor in exploiting the instruction-level parallelism (ILP) of programs. To alleviate this problem, many processors follow a hybrid von Neumann/dataflow computing model in which specific instruction sequences are executed in dataflow order by communicating intermediate values directly from producer processing units (PUs) to consumer PUs without using a central register file. However, the intermediate values often reside in local registers of the PUs, which requires a synchronization of the data transports that still limits the exploitation of the ILP.To avoid the use of a central register file and the need for any synchronization between PUs, some newer architectures suggest first-in-first-out (FIFO) buffers instead of local registers at the input and output ports of the PUs. Since values are produced and consumed, and are thus never overwritten (as in registers), the compiler must determine the required number of copies of each value. Furthermore, it is necessary to control the number of copies of values to develop buffer size aware compilation methods. However, the number of variable uses in a sequential program may depend on the future execution. This paper presents transformations for ‘balancing’ a given program, i.e., transforming the program so that for all points in the program, the number of future uses of all variables can be accurately determined in order to allocate the required buffer sizes in the later compilation phases. The classical space-time trade-off is demonstrated by the experimental results which show an improvement of the processor performance with increasing buffer sizes and vice versa. More importantly, the experimental results demonstrate the potential of buffered hybrid dataflow architectures for a scalable use of ILP.
A Reliability Analysis of Self-Driving Vehicles: Evaluating the Safety and Performance of Autonomous Driving Systems

Self-driving cars are a ground-breaking invention with the potential to revolutionize the transportation sector. As technology develops, it is more crucial than ever to guarantee the dependability of self-driving cars. The dependability analysis of self-driving cars is the main topic of this research article. The multiple levels of automation in self-driving cars and their accompanying reliability requirements are covered in the first section of the study. Afterwards, it examines the many parts of a self-driving car system, such as the perception, decision-making, and control subsystems, and talks about the dependability issues that each of these parts faces. The paper ends by highlighting the value of reliability analysis in ensuring the security and wide acceptance of self-driving cars. In order to overcome the remaining issues and provide more sophisticated methods for self-driving car dependability evaluations, the report advocates for additional research.
Real-time Method of Accurate Unique IPs Counting Across High Number of Distinct Dimensions and Distinct Time Frames for Big Data Systems.

The article describes a method which allows counting unique IP addresses within 10 bln of sys-tem events per day across high number of distinct dimensions (tuples). Log-based and probabi-lity-based methods showed unsatisfactory results. The proposed method allows avoiding excessive resource usage (RAM, CPU and persistent storage) as it appeared in a raw logs method and a probability method of counting. The method also avoids high statistic error for low cardinality as it appeared in a probability method. The main idea is to count unique IP addresses in distinct tuples in real time using RAM for short data interval processing, then flushing it to persistent storage, using merge algorithms to process and store unique IP counts in ordinary database from 5 minute, hourly, daily, weekly and monthly interval files
Reputation-Based Trust Assessment of Transacting Service Components

We witness a paradigm shift where entities such as software applications, people, businesses and service providers increasingly interact in virtual rather than physical environments, such as social media, social commerce, and the metaverses. A key issue that emerges in such environments is how one entity can trust another. Here, the concept of trust is considered as a meta-requirement, that is, the level of belief a service requestor has that a service provider will provide the service in a way that meets the requestor’s expectations. We refer to the service offering entities as service providers (SPs) and the service requesting entities as service clients (SCs). In this paper, we propose a technique that allows for evaluating trust and assigning reputation to various service providers by considering first their ability to fulfill their clients’ expectations or policies, and second the reputation other service clients have, when acting as recommenders for the aforementioned service providers. In this work, service clients and service providers are considered as virtual entities that coordinate with each other, and may include physical users, avatars, micro-services, software agents, smart contracts or any other distributed inter-networked resource, without making any assumptions as to what a service client or a service provider entity is as long as it participates in an interaction.
Evaluating Graphene and Molybdenum Disulfide Nanopores for DNA Sequencing

Graphene and molybdenum disulfide (MoS2) nanopores, with their superior characteristics and subnanometer thickness, are expected to be the next generation of rapid and low-cost methods of DNA sequencing. In this paper, focused ion beam (FIB) is employed to fabricate nanopores with similar sizes on these two-dimensional (2D) materials to compare their performance in sequencing DNA. According to our experimental results, graphene nanopores have a stronger interaction with DNA than MoS2, which contributes to a better temporal resolution. However, in the low-frequency region, the 1/f noise level of MoS2 is obviously lower than that of graphene. Furthermore, the nanopore was fabricated on graphene and MoS2 heterostructure for DNA sequencing, and the translocation events were successfully captured. It is demonstrated that the new heterostructure reduces the high noise level in the low-frequency region of graphene nanopores in DNA sequencing. It is expected that graphene and MoS2 can complement each other and facilitate the realization of single-base detection resolution in the future.
Been Here Already? Detecting Synchronized Browsers in the Wild

Browsers have become the most popular and used platform for accessing the web. Their wide and exclusive usage as a medium for doing several tasks in the Internet comes with serious security and privacy risks for the users. For example, it has been shown that web sites can employ browser fingerprinting and cross-device tracking techniques to de-anonymizing or profiling a user’s browser. On the other hand, browsers become richer in functionality by the years. One very convenient feature, introduced recently and being available to most major web browsers, is synchronizing the browsers on different devices. Browser synchronization allows users to share settings and preferences of their browser running on multiple devices (e.g., on their laptop and smartphone). In this paper, we are the first to deliver a framework that can be used by web site operators to detect if different HTTP requests, issued from different browsers, are actually requests performed by the same user through multiple synchronized browsers running on different devices. For detecting this, we reconstruct different sessions based on their requested resources, timestamps and cookies. In addition, we evaluate our methodology by conducting a user study that collects anonymized HTTP requests from several users, and we prove that the detection of synchronized sessions is possible with a success rate higher than 75%. Our results indicate a serious implication to users’ privacy that has not been studied before.
Operator Splitting Solution of Equal Width Wave Equation Based on the Lie-Trotter and Strang Splitting Methods

In the present work, we have applied four different algoritms based on the Lie-Trotter and Strang splitting methods to obtain numerical solution of Equal Width (EW) equation. For this purpose, EW equation is split up into two sub equation which one is linear and the other is nonlinear and then cubic B-spline collocation finite element method applied to each sub equation. The main advantage of this method is to obtain simpler and easier to solve sub-equations. The accuracy of the suggested method is displayed by calculating error norms $L_{2}$, $L_{\infty }$ and conservation laws on the solution of a single wave motion. It was seen that cubic B-spline collocation schemes obtained via Lie-Trotter and Strang splitting methods led to\ lower error norms and quate easy to implement. The stability analysis of obtained schemes are investigated by von Neumann (Fourier Series) method in accordance with the structure of splitting methods. We considered single wave motion and Maxwellian initial pulse to examine the numerical solutions of the EW equation and to compare it with other studies.
Simulation of a Modular Multilevel Converter with Reverse Model Predictive Control

In this paper the modular multilevel converter (MMC) is tied to the utility grid and converts the dc output signal of PV panels to an ac signal. The challenges related to this type of inverters are balancing submodule capacitor voltages, suppression of the circulating currents, and tracking references of output currents. To attain these targets simultaneously, a control strategy based on the reverse model predictive control (R-MPC) is adopted. This control method reduces the computational burden by calculating the number of inserted MMC's submodules directly, based on the reverse prediction of arm voltages. Simulation of the converter dynamic behavior including the related control technique is conducted through MATLAB/Simulink and results validated the desired system performance.
Modulation of Joint Stiffness for Controlling the Cartesian Stiffness of a 2-DOF Planar Robotic Arm for Rehabilitation

This paper presents a method for achieving Cartesian stiffness control at the endpoint of a 2-degree-of-freedom planar robotic arm by modulating joint stiffnesses. Planar robotic arms are widely applied for upper-limb rehabilitation through impedance control, but not generally in Cartesian stiffness control through joint stiffness. A modular robotic actuator with integrated controllers on a robot prototype enables the direct command of desired joint stiffness. A closed-form solution was derived through the Jacobian matrix to map the stiffnesses of a reference equilibrium. In addition, the prediction of the joint displacement corresponding to the endpoint motion is required for computing the needed joint stiffnesses. The proposed method is experimentally validated by recording the Cartesian force against the unidirectional displacement at different robotic arm configurations, showing a linear relationship. The results suggest that the proposed method has the potential for use in rehabilitation tasks when the direction of the endpoint displacement is predetermined. The method allows a precise control of the robotic arm’s stiffness, which can help in creating more efficient rehabilitation protocols on an easily accessible and affordable rehabilitation robot. Nonetheless, further work is needed to improve the accuracy and omnidirectional robustness of the control method. The study also highlights the importance of designing a robotic arm to satisfy stiffness requirements in addition to kinematic optimization for sufficient workspaces.
Pre-AI Musical Style Analysis Via Their Spectral Distributions

In this paper, we will revisit a pre-AI method of identifying musical genres. To do that, the spectrograms of a number of fragments of melodies belonging to various genres will be generated, with the aid of the spectrum analyser included in the Digital Signal Processing Toolbox from Simulink, Matlab, as well as the spectrum analyser of the audio editing and analysis program Audacity. Then, there will be an attempt at identifying the defining spectral features for each of the analysed genres, with the aim of identifying said genres via Fourier analysis. Included in this paper will be a revision of key theoretical concepts pertaining to the Fourier analysis of analog signals as well as digital, as well as an enumeration of the relevant musical theory concepts.
Improved Bayesian network differential privacy data-releasing method based on junction tree

Ensuring personal privacy while releasing data is a crucial issue in privacy protection. Differential privacy is one of the popular privacy-protection methods, but it is difficult to apply it to high-dimensional data due to the curse of dimensionality. Although many differential privacy methods exist to handle high-dimensional data, they still have limitations. Firstly, this paper introduces the FAPrivBayes algorithm to expedite the generation of Bayesian networks. The algorithm employs node weighting to determine the order in which nodes join the network, effectively reducing the network generation time. Secondly, the paper presents an enhanced Bayesian network algorithm, JTFAPB, based on the JTree algorithm. This approach refines the network obtained by the FAPrivBayes algorithm and increases the privacy budget allocation for each subnet by reducing the number of subnets therein. Through theoretical analysis and experimental evaluation, the proposed method diminishes computational overhead and enhances the usability of published data while safeguarding personal privacy.
Facial Expression Recognition Using Deep Neural Network

Facial Expression Recognition (FER) system analyzes a person's emotions using facial expressions. Nonverbal communication cues, such as facial expressions and gestures, can be seen in FER. FER has applications in various areas, such as medical diagnosis and treatments, human resources, police investigation, education, customer service, journalism, and more. Nowadays, Convolutional Neural Networks (CNNs) play an important role due to their inherent feature extraction mechanism from images. This work utilizes a CNN to classify facial expressions into seven classes: anger, disgust, fear, happiness, sadness, surprise, and neutral. Experiments are conducted with different CNN models and pre-trained models on the FER-2013 dataset. We explore how the model accuracy changes with the model parameters and compare our classification results with existing CNNs. The experimental results demonstrate that the proposed CNN model, trained on a balanced dataset with data augmentation and batch normalization, achieves a training accuracy of 80% and a test accuracy of 72%. Furthermore, the experimental results show that EfficientNet-B1 can achieve a training accuracy of 93 % and a test accuracy of 86 % on a smaller subset of the dataset containing three emotion classes, which is more accurate than the state-of-the-art models.
6G Initial Developments for Flagship Applications Experiments

As the world transitions into the next generation of wireless communication technology, namely 6G, the potential for revolutionary advancements in connectivity and data transfer becomes increasingly evident. With the growing demands for higher bandwidth, ultra-reliable low-latency communications, and massive machine-type communications, 6G is expected to address the limitations of existing networks and unlock new possibilities for transformative use cases in the year 2030. This research paper aims to provide a comprehensive analysis of the new software requirements needed for the transition towards future networks, drawing on currently available 5G Stand-Alone (SA) experimentation testbeds that implement the 3GPP Release 16 standard. It also looks at the new envisioned use cases for 6G, developed in the context of EU founded research and innovation projects, focusing on the projected trends, requirements and initial developments that will shape the 6G landscape. Additionally, with its focus on the KPIs needed for B5G and 6G applications, the paper highlights the pressing need for an updated architecture to accommodate the unique challenges posed by the future cellular networks standards.
Research On Multipath Separation And Parameter Estimation Algorithm Based On DHA

Multipath fading can degrade wireless communication performance significantly. While detailed multipath parameters can be used to resist fading effectively. However, separating multipath and reducing the high cost of uniform linear array (ULA) is a challenge. In this paper, we exploit the current advances of dynamic heterogeneous array (DHA) which rapidly changes patterns within a single pilot symbol period to overcome these problems. DHA can be used to realize the multiple measurements with only one radio frequency (RF) chain due to its ability in constructing a space isomeric array for producing different direction patterns. Then, after recovering the element-space array data, we propose an approach based on the weighted subspace fitting (WSF) method to estimate the parameters for each propagation path, including the angle of arrival (AoA) and complex gain. Simulation results show significant improvement compared to ULA.
Admittance-Based Non-Singular Terminal Sliding Mode Control of Multiple Cooperative Manipulators

This paper introduces a novel decentralized control framework that integrates admittance control, non-singular terminal sliding mode (NTSM) control, and load distribution to cooperatively manipulate a single object with multiple robotic manipulators. Admittance control grants the system a level of compliance for safe human-robot physical interaction. The admittance controller maintains all communication between the manipulators. The NTSM controller provides accurate tracking of the manipulators in the presence of disturbances and dynamic model uncertainties. A decentralized load distribution method is designed to minimize the internal forces on the object. The proposed framework is applied to numerical simulations of a team of three 3-DOF robotic manipulators to demonstrate its effectiveness in cooperative manipulation tasks.
Design and Implementation of a QoL Improvement System for Visually Impaired Patients

Visually impaired people encounter several challenges in their mobility and navigation. Their daily activities are obstructed due to their inability to adapt or recognize accurately their surroundings, especially outside their house which they are more familiar with. Thus, it becomes the main reason of accidents, falling off, getting lost in unknown areas, etc. Furthermore, one of the sensory systems that helps the body to process data about the external environment is the visual system. Blind people also lose touch with the outside world, develops poor motor habits, which results in postural problems as a result. This project will assist visually impaired people in their daily life and simplify normal tasks through a system combining two previously designed projects, “Smart Shoes for Blind and Visually Impaired People”, and “Human posture monitoring device”. The multifunctional system is developed with the goal of securing safe movements for visually impaired people as well as maintaining a good back posture by detecting leaning postures (LP). The purpose of the smart shoe is to identify obstacles and protect the user from unintended accidents. A compatible Android application has been developed to alert the user when there is an obstruction or when he is walking on a wet surface. Voice alarms will be used to acoustically alert the user. If the user collapses, a message with their position will be sent right away to a relative. On the other hand, the smart vest will identify the position of the user's back and alert him to maintain a straight posture through the same application as well. As the system is dealing with human health, some safety measurements would be taken into consideration to implement a safe electrical system in order to reduce error and to increase accuracy.
Deep Neural Network Design for Improving Stability and Transient Behavior in Impedance Control Applications

Robot manipulation of the environment often uses force feedback control approaches such as impedance control. Impedance controllers can be designed to be passive and work well while coupled to a variety of dynamic environments. However, in the presence of a high gear ratio and compliance in manipulator links, non-passive system properties may result in force feedback instabilities when coupled to certain environments. This necessitates an approach that ensures stability when using impedance control methods to interact with a wide range of environments. We propose a method for improving stability and steady-state convergence of an impedance controller by using a deep neural network to map a damping impedance control parameter. In this paper, a dynamic model and impedance controlled simulated system are presented and used for analyzing the coupled dynamic behavior in worst case environments. This simulation environment is used for Nyquist analysis and closed-loop stability analysis to algorithmically determine updated impedance damping parameters that secures stability and desired performance. The deep neural network inputs utilized present impedance control parameters and environmental dynamic properties to determine an updated value of damping that improves performance. In a data set of 10,000 combinations of control parameters and environmental dynamics, 20.3% of all the cases result in instability or do not meet convergence criterion. Our deep neural network improves this and reduces instabilities and failed control performance to 2.29%. The design of the network architecture to achieve this improvement is presented and compared to other architectures with their respective performances.
Spatio-temporal Analysis of Dashboard Camera Videos for Time-To-Accident Forecasting

Globally, traffic accidents are the leading cause of death according to reports published by the World Health Organization (WHO). Advanced Driver Assistance Systems (ADAS) are effective in reducing the likelihood or severity of accidents. However, such systems are expensive and inaccessible to the majority of the population. Time-To-Accident (TTA) forecasting is a critical component of ADAS systems as it can allow for better decision-making in traffic, improve dynamic path planning, alert the driver of potential dangers, and more. Sensor or depth imaging-based TTA forecasting can be inaccurate and are unaffordable to the mass. Existing vision-based traffic safety research in the literature focuses on accident anticipation with limited work on TTA prediction. We propose forecasting TTA by utilizing spatio-temporal features from dashboard camera videos and introducing a novel algorithm to compute ground truth TTA values. Additionally, we present a CNN-Transformer Hybrid (CTH) model that overcomes the limitations of end-to-end CNN or Transformer-based models. Our best models can predict TTA values with an average prediction error of 0.24 and 0.80 seconds on the CCD and DoTA dataset respectively. Furthermore, We assess our approach's ongoing scene recognition ability using the TTA-ASiST@x (Time-to-Accident-based Accuracy at the $\mathrm{x}^{th}$ frame Since Scene Transition) metric. In real-world scenarios, it is vital to identify when a normal driving scene transitions into a potential accident scene. With just 25% of the data, HyCT achieved over 85% accuracy in recognizing accident and non-accident scenes, further showcasing its performance on limited data.