Miracle-dz/UniFAR-FTUs huggingface dataset:
Green and Secure Computation Offloading for Cache-Enabled IoT Networks

The ever-increasing number of diverse and computation-intensive Internet of things (IoT) applications is bringing phenomenal growth in global Internet traffic. Mobile devices with limited resource capacity (i.e., computation and storage resources) and battery lifetime are experiencing technical challenges to satisfy the task requirements. Mobile edge computing (MEC) integrated with IoT applications offloads computation-intensive tasks to the MEC servers at the network edge. This technique shows remarkable potential in reducing energy consumption and delay. Furthermore, caching popular task input data at the edge servers reduces duplicate content transmission, which eventually saves associated energy and time. However, the offloaded tasks are exposed to multiple users and vulnerable to malicious attacks and eavesdropping. Therefore, the assignment of security services to the offloaded tasks is a major requirement to ensure confidentiality and privacy. In this article, we propose a green and secure MEC technique combining caching, cooperative task offloading, and security service assignment for IoT networks. The study not only investigates the synergy between energy and security issues, but also offloads IoT tasks to the edge servers without violating delay requirements. A resource-constrained optimization model is formulated, which minimizes the overall cost combining energy consumption and probable security-breach cost. We also develop a two-stage heuristic algorithm and find an acceptable solution in polynomial time. Simulation results prove that the proposed technique achieves notable improvement over other existing strategies.
Performance Analysis of Dual-Hop RF-UWOC Systems

In this paper, we analyze the performance of a dual-hop radio frequency-underwater wireless optical communication (RF-UWOC) transmission systems wherein the RF and UWOC links experience Nakagami-<inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> fading and the mixture Exponential-Generalized Gamma fading, respectively. The location of <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> is uniformly distributed in the space of the hemisphere where the relay is located in the center of the hemisphere. The effect of bubbles level, temperature gradient, water types, and detection techniques are considered. We derive closed-form expressions for outage probability (OP) and average bit error rate (ABER) for both fixed and variable gain relaying schemes with different detection techniques. Furthermore, by utilizing the expansion of Meijer's <inline-formula><tex-math notation="LaTeX">$G$</tex-math></inline-formula>-function and Fox's <inline-formula><tex-math notation="LaTeX">$H$</tex-math></inline-formula>-function, the closed-form expressions for the asymptotic OP and ABER are derived when the average signal-to-noise ratio of both links tends to infinity. The analytical results are verified by Monte Carlo simulation results. Our results demonstrate that the diversity order of the dual-hop RF-UWOC systems depends on the RF fading parameter and detection technology of the UWOC link.
3D-CNN with residual sub-blocks for automatic detection of lung nodules from temporal subtraction images

Temporal subtraction (TS) technique is one of computer aided diagnosis (CAD) systems. A TS image is obtained by subtracting a previous image, which are warped to match between the structures of the previous image and one of a current image, from the current image. TS technique removes normal structures and enhances interval changes. However, many subtraction artifacts that can be detected as false positives still remain on a TS image. In this paper, we propose 3D-CNNs with residual sub-blocks based on 3D-VGG16-like architecture for detection of nodules accurately from TS images.
Cost-Efficient Flexible Supercapacitive Tactile Sensor With Superior Sensitivity and High Spatial Resolution for Human-Robot Interaction

Tactile sensing is crucial for the safety, accuracy and robustness of the human-robot interactions in the fields of wearable equipment, service robots and healthcare robots. Although many efforts have been made, it still requires much work to develop functional and reliable flexible tactile sensors with superior sensitivity, wide measurement range, high spatial resolution and low cost based on simple structures and easy fabrication. Here, this paper introduces a flexible supercapacitive tactile sensor with outstanding and balanced performance. The tactile sensor contains two layers of flexible electrodes and a layer of ionic-gel coated microfiber matrix to form a supercapacitive sensing structure. The flexible electrodes and the ionic microfiber matrix are processed with scalable techniques such as screen printing and gel-coating, which guarantee the ultra-flexibility and low fabrication cost. The experimental data suggests strong linearity in the pressure-capacitance relationship and high sensitivity (135.9 nF<inline-formula> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula>kPa<inline-formula> <tex-math notation="LaTeX">$^{-1}\cdot $ </tex-math></inline-formula>cm<sup>2</sup> or 27.11 kPa<sup>−1</sup>). Wide pressure measurement range (from 0 kPa to 1200 kPa) is also achieved by balancing structure parameters. Dynamic responses of the tactile sensors could accurately reflect the applied pressure cycles from human-finger tapping and machine pressing. The tactile sensor can map the pressure distribution with a high spatial resolution (>2 points<inline-formula> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula>mm<sup>−2</sup>) when connected with the specially designed electric circuitry. The spatial resolution from sub-mm to large area makes it promising for various sensing applications in human-robot interactions, from finger touch to body contact. The developed tactile sensor in this study owns superior applicability and universality which makes it a trustworthy candidate to benefit various applications in robotics, flexible electronics and bioengineered equipment.
Anomaly Detection by Learning Dynamics From a Graph

There exist relations, which vary with time or by an event, between high dimensional elements in most real-world datasets. A dynamic graph or network has been used as one of the remarkable approaches to represent and analyze them. In spite of the advantages of representing data in the form of graphs, it is difficult to apply representation (deep) learning to graphs. Recently, AlphaFold by DeepMind has shown remarkable results in applying deep learning to graphs. This research is part of the current effort to extend the input domain of deep learning to arbitrarily graphs and their dynamics of variations. In this paper, we propose a method to predict the evolution of graphs by learning spatio-temporal features called dynamics. The method involves two main processes: extracting spatial features from static graphs obtained at different times and learning temporal features from the time-varying connection structure. Instead of predicting the overall changes of a highly complex graph, we detect the dynamic anomaly by predicting the affinity score with respect to a node (e.g., a hub as an important factor) of a dynamics graph. This facilitates the learning dynamics of graphs having sparsity of connections by alleviating the curse of dimensions using the fact that most graphs of real-world problems are scale-free. To justify our approach, we apply our method to real-world problems such as computer networks and public transportation. Experimental results show that our approach is competitive with other existing methods.
Distributed SGD With Flexible Gradient Compression

We design and evaluate a new algorithm called FlexCompressSGD for training deep neural networks over distributed datasets via multiple workers and a central server. In FlexCompressSGD, all gradients transmitted between workers and the server are compressed, and the workers are allowed to flexibly choose a compressing method different from that of the server. This flexibility significantly helps reduce the communication cost from each worker to the server. We mathematically prove that FlexCompressSGD converges with convergence rate <inline-formula> <tex-math notation="LaTeX">$1/\sqrt {MT}$ </tex-math></inline-formula> where <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> is the number of distributed workers and <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> is the number of training iterations. We experimentally demonstrate that FlexCompressSGD obtains competitive top-1 testing accuracy on the ImageNet dataset while being able to reduce more than 70% communication cost from each worker to the server when compared with the state-of-the-art.
Policy Gradient Based Control of a Pneumatic Actuator Enhanced with Monte Carlo Tree Search

This paper presents a synergy of the Monte-Carlo tree search (MCTS) and a reinforcement learning (RL) based control strategy to achieve the position control of an electropneumatic gearbox actuator. Besides tracking the reference signal, there are qualitative requirements regarding the switching time and the overshoot, and there is also a necessity of reliable behavior in a wide range of operating conditions. By utilizing the domain-specific knowledge of a trained agent, the direction of the tree search can be controlled, hence the quality of the RL control can be further enhanced by the robustness of the MCTS algorithm.
Learning Robust Shape-Based Features for Domain Generalization

Domain generalization is a challenging problem of learning models that can generalize to novel testing domains which are unavailable during training and follow different distributions from training domains. In this paper, we introduce a simple but effective method for domain generalization, which is based on the object shape hypothesis. The main idea is from some recent studies which demonstrate that leading the network to focus more on the object shape and recognizing objects through shape-based features are more significant and robust. To achieve this, we first stylize source domain images into randomly sampled styles through a style transfer network and then use both the stylized images and original source domain images to learn the classification model. With images of the same content but different styles (textures), we expect the network to classify according to the global object shape rather than local textures and further transfer the object shape knowledge to novel domains. By this way, the generalization ability of the classification model will be improved. We conduct extensive experiments on PACS, VLCS and OfficeHome benchmarks and both the quantitative and qualitative analysis demonstrate the effectiveness of the proposed method.
Path Planning Techniques for Mobile Robots A Review

Mobile robots have become increasingly popular in recent years, offering a wide range of applications in areas such as industry, agriculture, search and rescue and much more. This has been achieved mainly as a result of extremely active research and development work on robotic and autonomous technology. We are still faced with many challenges however in order for a robot to navigate efficiently and reliably in an environment without any human assistance. The robot should be capable of extracting the necessary information from the environment and taking the necessary action required to plan a feasible path for collision free motion to reach its goal. In this paper, we review the most commonly used path planning methodologies that have been applied for mobile robot navigation in both static and dynamic environments. We look at both global and local path planning approaches as well as classical and heuristic based techniques.
Experiences with ML-Driven Design: A NoC Case Study

There has been a lot of recent interest in applying machine learning (ML) to the design of systems, which purports to aid human experts in extracting new insights leading to better systems. In this work, we share our experiences with applying ML to improve one aspect of networks-on-chips (NoC) to uncover new ideas and approaches, which eventually led us to a new arbitration scheme that is effective for NoCs under heavy contention. However, a significant amount of human effort and creativity was still needed to optimize just one aspect (arbitration) of what is only one component (the NoC) of the overall processor. This leads us to conclude that much work (and opportunity! ) remains to be done in the area of ML-driven architecture design.
Translation and Psychometric Properties of the Persian Version of Oxford Non-technical Skills 2 System: Assessment of Surgical Teams' Non-technical Skills in Orthopedic Surgery Wards.

Background Non-technical skills are interpersonal and cognitive skills involved in safe performance and preventing adverse events during surgery. it is necessary to dominate the non-technical skills to ensure patient safety. This study has aimed to assess the validity and reliability of Oxford Non-technical skills 2 system (Oxford NOTECHS 2) in Iran and to evaluate surgical teams' non-technical skills in orthopedic surgery wards. Methods This cross-sectional study was conducted in Tehran, Iran during 2015. The level of evidence is III based on Canadian Task Force on the Periodic Health Examination. We followed the Beaton's guideline for Persian translation and cross-cultural adaptation of the checklist. In this study, 60 orthopedic surgical team members working in two selected public hospitals were selected by cluster random sampling method.Oxford NOTECHS 2 system which is consisted of four subscales including leadership and management, teamwork and collaboration, decision-makingand problem-solving, and situational awareness was used to collect the data. Results The overall mean score of non-technical skills was 69.52±6.64. The mean score for surgery, anesthesia, and nursing sub-teams were 24.98±3.71, 21.12±4.29, and 23.42±3.60, respectively. The teams' scores in total, leadership and management, teamwork and collaboration, problem solving and decision making, and situational awareness at the standard level were 74.70%, 76.95%, 73.75%, 66.87%, and 74.70% of maximum score, respectively. Conclusion The validity and reliability of the Persian version of Oxford NOTECHS 2 scale in Iran was confirmed. The results of this study showed that surgical teams' non-technical skills were at a moderate level in orthopedic surgery wards. The minimum score of the surgical teams' non-technical skills belonged to anesthesia and maximum to surgery sub-team. Using the training programs and setup workshop is recommended to improve the surgical teams' non-technical skills, especially surgery-nursing sub-team.
A Delay-Tolerant Data Transmission Scheme for Internet of Vehicles Based on Software Defined Cloud-Fog Networks

The low-latency advantages of fog computing can be applied to solve high transmission latency problems of many network architectures in Internet of Vehicles. Therefore, this paper studies the application of fog computing in Internet of Vehicles. Considering that the fog network equipment deployed in Internet of Vehicles is relatively scattered, a new network architecture is proposed, which integrates cloud computing, fog computing and software defined network and other technologies. The proposed framework uses software defined network to centrally control fog network and obtains equipment performance of fog network. Furthermore, the optimal load balancing strategy is developed by communication overhead and other information. Based on time delay modeling of fog network, we study the time delay modeling of cloud-fog network and the energy consumption modeling of fog network. In addition, this paper models the selection process of data transmission network and data calculation execution server of delay-tolerant data as a partially observable Markov decision process optimization strategy in software defined Internet of Vehicles. By observing the state of system, current storage makes optimal decisions on data transmission and selection of computing nodes, thereby minimizing system overhead. Simulation results show that the proposed scheme can effectively reduce transmission delay and system overhead, improve data calculation efficiency.
A Time Sharing Hybrid Probabilistic Shaping Scheme for Nonbinary LDPC Codes

A time sharing (TS) hybrid probabilistic shaping (HPS) scheme for nonbinary (NB) LDPC codes is proposed. For the conventional probabilistic shaping (CPS) scheme, the demodulator uses bit-metric decoding (BMD) for binary LDPC codes and NB LDPC codes. However, the demodulator for NB LDPC codes adopts symbol-metric decoding (SMD) on the AWGN channel to avoid the loss of symbol-to-bit mapping and obtain better performance than binary LDPC codes. In order to take advantage of SMD demodulation for NB LDPC codes, the NB-LDPC information symbols use constant composition distribution matcher (CCDM) to generate quadrature amplitude modulation (QAM) symbols with unequal probability distribution, while the NB-LDPC parity symbols are utilized to yield QAM symbols with approximately uniform distribution. The SMD demodulation of the proposed NB-LDPC-coded TS HPS scheme has lower demodulation complexity than the NB-LDPC-coded CPS scheme. The information-theoretical analysis of the CPS system and the TS HPS system turns out that the proposed NB-LDPC-coded TS HPS scheme is superior to the NB-LDPC-coded CPS scheme for 8QAM and 32QAM format. Simulation results verify the theoretical average mutual information (AMI) analysis and show that the proposed NB-LDPC-coded TS HPS scheme has up-to-0.24 dB and 1.10 dB performance gain compared with the NB-LDPC-coded CPS scheme for 8QAM and 32QAM format, respectively. Compared with the binary LDPC-coded regular Nyquist scheme for 8QAM and 32QAM format, the proposed NB-LDPC-coded TS HPS scheme has up-to-1.31 dB and 2.04 dB performance gain, respectively. Meanwhile, the LDPC code rate of the proposed TS HPS scheme is more flexible than the CPS scheme. Therefore, the proposed NB-LDPC-coded TS HPS scheme is more reliable, efficient, and flexible for 8QAM and 32QAM, and thus suitable for the sixth generation (6G) communication system.
Leakage Current Monitoring of Suspension Insulator for Effective Determination of ESDD

Leakage current contains significant characteristics of a polluted insulator. With the increase in pollution the magnitude of leakage current increases. Equivalent Salt Deposit Density (ESDD) is a popular parameter for the estimation of the severity of pollutants. However, the measurement of ESDD involves costly equipment and requires samples directly from the surface of suspension insulator. It is a tedious task to collect a sample from the surface of the suspension insulator under operation. In the present work, a methodology has been proposed to estimate the contamination level severity parameter, ESDD using Detrended Fluctuation Analysis (DFA) on recorded leakage current data. In the present work, NaCl, Kaolin, and water are used to emulate the artificial pollutant layer on an 11 kV suspension insulator disc. It is observed that the DFA parameter maintains a well-defined relationship with ESDD and conductivity.
Smart Vehicle Protocol Monitoring System by the Internet of Things Platform

Traffic jam has a grim impact on everyday life. Time wasting and safety are the main concern for this menacing problem. To develop safety in the road, the Internet of Things (IoT) is a potential solution. This paper represents a design and implementation of a Radio Frequency Identification (RFID) and ESP8266 Wi-Fi module based smart traffic monitoring system which effectively identified every vehicle on the road with their respective speed, phone number, license and expires date. After that, by creating a local server all the sensor data are stored in the server and showed on the website. The work is also constituted a $16\times 2$ LCD display as a local monitoring system which will display only the speed of the incoming vehicles and the license number of the car with the help of ultrasonic sensors. In this system, the detection accuracy of the vehicle is 97.42%. Finally, this project estimates the data with graphical analysis. So, the whole process connects the vehicle users with an IoT monitoring system.
Testing Method for Software With Randomness Using Genetic Algorithm

The main task of software testing is the generation of test data with high quality in restricted time. But for software with uncertainties, such as randomness, existing methods of generating test data often lose their effectiveness. For software with randomness, a test datum might cover the test target in one run, but obtains different results in the next run. Therefore, special testing methods must be exploited for this kind of software. In order to test software with randomness, we first propose a novel test adequacy criterion, and then build a mathematical model for generating test data according to the new criterion. Finally, we present a method of solving the optimization model using a set-based genetic algorithm. We apply the proposed method to test 12 programs, and compare with traditional genetic algorithm and the random method. From the experimental results we can see that, the proposed adequacy criterion is available for the software with randomness and the proposed algorithm can effectively generate test data.
Developing a Bayesian Network Framework for Root Cause Analysis of Observable Problems in Cyber-Physical Systems

Because critical infrastructures rely on Cyber-Physical Systems (CPSs), appropriate response to problems in such infrastructures operated by CPSs is important. Firstly, it is essential for decision-makers to be able to determine whether the observed problem is due to an attack or technical failure. In previous work, we developed a framework for building Bayesian Network (BN) models to enable decision-makers to determine whether the observed problem is due to an attack or technical failure. However, this information alone is not adequate to choose effective response strategies for the observed problem. It is also essential for the decisionmakers to be able to determine the most likely attack vector used to cause the observed problem or failure mode caused the observed problem to choose effective response strategies. However, the decision support to determine the most likely root cause for an observed problem is missing. In this paper, we develop a framework for building BN models to enable decisionmakers to determine the most likely root cause of problems. We demonstrate the developed framework using an example problem in smart grids.
Improving the Pedal Force Effectiveness Using Real-Time Sonification

The pedal force effectiveness, an important parameter in cycling performance, is rarely optimal. Decreased power is generally observed during the recovery phase (the cyclist does not pull enough). An essential part of training for cyclists consists in improving pedaling technique. Visual feedback can be useful but may not be feasible in real life, where the cyclist has to visually focus on the road. We propose auditive feedback as a better way to help cyclists improve their pedal force effectiveness in real-time. In this study, both competitive cyclists and non-cyclists tested different online sonification strategies of force effectiveness, comparing them to a silent control. The ”Squeak” strategy, generating a squeak when torque was negative, had the most positive impact on Torque Effectiveness: some participants managed to eliminate any negative torque for a full minute.