Miracle-dz/UniFAR-FTUs huggingface dataset:
Kinematic Analysis of an Under-constrained Cable-driven Robot Using Neural Networks

In this paper, three types of neural networks including, multilayer perceptron, radial basis function, and Local Linear Model Trees, are considered based on adaptability with the significant behavior of data to solve the forward and inverse kinematics of the under-constrained cable-driven parallel robot. The practical workspace of the robot is a rectangular cube with dimensions of 360cm × 220c× 160cm. Forward kinematics problem of the under-constrained cable-driven parallel robot is challenging to solve by reason of complexity and nonlinearity of the robot kinematic equations. The typical approach utilizes a numerical method to solve these equations, which are computationally expensive and very time-consuming. Resorting to the neural network approaches results in the feasible orientation, which confirms that all the cables are in tension. The trained neural network gives a feasible inverse kinematic problem solution and the running time of the optimal trained neural network in the control loop, obtained about 12 microseconds, pales in comparison to the sampling time of the control loop that facilitates the real-time control of the robot. Obtained results reveal that the multilayer perceptron has the mean average error order of about 2 millimeters for the position and 0.78 degrees for orientation, which makes a case for precise control in the high-speed motion of the robot.
Unsupervised Machine Learning Techniques for Network Intrusion Detection on Modern Data

The rapid growth of the internet, connecting billions of people and businesses, brings with it an increased risk of misuse. Handling this misuse requires adaptive techniques detecting known as well as unknown, zero-day, attacks. The latter proved most challenging in recent studies, where supervised machine learning techniques excelled at detecting known attacks, but failed to recognize unknown patterns. Therefore, this paper focuses on anomaly-based detection of malicious behavior on the network by using flow-based features. Four unsupervised methods are evaluated of which two employ a self-supervised learning approach. A realistic modern dataset, CIC-IDS-2017, containing multiple different attack types is used to evaluate the proposed models in terms of classification performance and computational complexity. The results show that an autoencoder, obtained from the field of deep-learning, yields the highest area under the Receiver Operating Characteristics (AUROC) of 0.978 while maintaining an acceptable computational complexity, followed by one-class support vector machine, isolation forest and principal components analysis.
Synthesis of Large-Area MoS2 Sword-Like Flakes by Chemical Vapor Deposition

Recently, the emerging field of two-dimensional materials is known to be promising due to their unique structure, special optical and electrical properties, and their applications in optoelectronic devices. In this paper, for the first time, we are reporting on new MoS2 samples with slightly different structures to that of the conventional ones. This is achieved using chemical vapor deposition (CVD) method and further changing of argon flux, furnace temperature, and precursors' distance to the location of substrates. This structure is sword-like, and it should be noted that length of some of these sword-like specimens is measured to be ≅700 micrometres. The broad dimensions of these samples may simplify the manufacturing process of the electronic components. The CVD reaction used here is carried out inside a furnace with one thermal zone and using sulfur and MoO3 as precursors. By changing the three effective growth parameters: argon flux, scraping the substrate surface (silicon oxide), and synthesis time, an optimum condition for the growth of the sword specimens is obtained. It is found that increasing argon flux up to a specific limit (200 SCCM), increases the length of the sword specimens, however, passing this limit, the length of the sword samples will stop increasing. Scratching the substrate's surface causes the sword-like specimens to accumulate in the scratched region. Furthermore, increasing the synthesis time results in the growth of more samples on the substrate's surface and creates a change in the samples crystalline form and transforms them into the fractal-like structure.
Power Allocation in Cellular Network Without Global CSI: Bayesian Reinforcement Learning Approach

Optimal power allocation in dense wireless networks is essential for managing both inter-cell and intra-cell interferences to maximize the network's total rate. Reinforcement Learning (RL) has been used in power allocation problem for maximizing capacity, but in this work, we have considered downlink power allocation for each user using only its own Channel State Information (CSI). Prior studies used CSI form neighbor cells, and it causes overhead in signal and makes delay in the network. However, in a real scenario, it is not always possible to get CSI from neighbor cells. Assuming that the RL agent only has access to its CSI, the problem becomes partially observable. Thus RL exploration becomes very difficult, and traditional RL methods do not have proper performance. To solve this problem, we have used a Bayesian RL method, Bayesby-Backprop Q-network (BBQN), for power allocation without needing CSI from neighbor cells. In this way, the Bayesian Neural Network (BNN) is used to obtain a posterior distribution over action-space value functions, and actions with higher uncertainty are explored more. Simulation results demonstrate the convergence and robustness of the BBQN, while traditional schemes fail to find an acceptable power assignment.
Investigation of Bismuth Doping Effects on CZTS Properties: A Density Functional Theory Study

Post-transition metal-doped quaternary chalcopyrite CZTS film has been proposed as a novel approach to improve solar energy capturing and conversion strategy. In this paper, lattice structural and optical properties of CZTS (Cu2ZnSnS4) in pure and Bi-doped conditions are theoretically investigated by employing first-principle density functional theory (DFT). Results indicate that lattice constant and bonding lengths do not change after adding bismuth substitution for a tin site. Projected density of states has been calculated for sulfur atoms on the first layer of adjacent Bismuth. Hybridization occurs between 6p state of bismuth and 3s state of sulfur atom. Furthermore, the PDOS diagram represents energy bandgap of 0.7eV and Fermi energy shift to the conduction band edge. An acceptor state is induced due to bismuth-tin substitution about 0.5 eV above the valance band edge. According to optical results extracting from dielectric functions, the absorption coefficient for bismuth-doped structure increases to 5.5×105cm−1 for low energy photons whereas, pure CZTS has no absorbed photon in this range. A slight decrement is exhibited in visible light reflection. Due to the acceptor state, the conductivity of carrier is facilitated for lower energy photons. Consequently, the substitution between bismuth and tin improved optical properties of CZTS as an absorber layer.
Driver Drowsiness Detection Based on Joint Monitoring of Yawning, Blinking and Nodding

Deaths and injuries resulting from road traffic crashes remain a serious problem globally and current trends suggest that this will continue to be the case in the foreseeable future. In this paper, we propose a reliable method towards drowsiness detection by analyzing images of the driver’s face. In fact, the shadows caused by wearing glasses and/or bad light conditions may, in particular, decrease the accuracy rate of the blinking detection. In addition, drowsiness symptoms are not just restricted to the frequency of blinking, as most of existing works do, but also include yawning and nodding. Thus, instead of using a single facial feature to predict the drowsiness state, we jointly combine eye closure and yawning by measuring the eye and the mouth aspect ratios, and head pose which is estimated by analyzing the optical flow. Then, we investigate the multilayer perceptron and the K-nearest neighbors as two classification techniques. The proposed method has been tested on the challenging private NTHU-DDD benchmark video dataset, and the preliminary experimental results show the effectiveness of the proposed automated method.
A Hybrid Swarm Optimization-Based Algorithm for the Set-Union Knapsack Problem

In this paper, we propose a hybrid particle swarm optimization-based algorithm for approximately solving the set-union knapsack problem. Knapsack problem arises in several applications such as manufacturing systems, transportation and supply chain management. This study reinforce the basic swarm optimization method with a local search procedure for the set-union knapsack problem. The proposed approach is evaluated on a benchmark instances available in the literature. The bounds achieved by the proposed approach are compared to those provided by the best methods available in the literature. Encouraging results have been provided.
Overlapping Community Detection in Social Networks Using Cellular Learning Automata

The community detection problem in social networks has been broadly explored from different aspects. In this problem, it is attempted to divide a network into a group-set of similar nodes (communities), in which nodes inside a community structure reflect more similar functional characteristics or than that of other nodes outside the community. However, a typical characteristic of some real networks is that the presence of overlapping communities, where nodes can be a member of quite one community at an equivalent time. In this paper, an algorithm for overlapping community detection is proposed using cellular learning automata (CLA-OCD). In the CLA-OCD, a group of learning automata collaborates to find overlapping communities. The performance of the CLA-OCD is evaluated by several experiments, which is the design of both synthetic and real networks. Through experiments, the CLA-OCD achieves significant improvement in performance measures, including modularity, F-score, and normalized mutual information (NMI).
A decision support system for B2B CRM Systems using belief rules

The complexity of business environment often forces sales representative to take decisions using intuition and/or subjective models created through experience instead of data driven decisions. However, data driven decisions generate better revenue. In this work, we present a methodology to create a Decision Support System (DSS) for Business to Business (B2B) sales that generates recommendations for actions to follow and actions to avoid to increase the chances of successfully closing a deal. At the core of our proposed DSS, is a three-step methodology relying on a particular approach of using belief rules. In the first step we generate, based on available data on previous won/lost deals, some forecasting models. As the best performing models are of black box type, the second step aims to extract if-then conventional rules that are converted, in the last step, into belief rules. Rather than using an attribute value probability matching to activate a belief rule, we propose a cost of activation that takes into account the current state of the active deal and the cost of transitioning the active deal’s attributes into a new state that could activate a belief rule. Furthermore, we propose and analyze two methods of calculating the cost of transition related to the effort the sales representative should dispose to move the current deal into the new state. The threestep approach was used to create a DSS for a real world B2B complex sales Customer Relationship Management Systems and the results are discussed and included in this paper.
Estimating 3D Coordinates of Bounding Box Center after Detection the Object in Image of Cluttered Environment Based on Stereo Vision and U-Net for navigate the robots

Recently deep learning attained to good achievements in various fields and stereo vision is also one of the most important methods for estimating 3D coordinates. In this paper, two methods of deep learning and stereo vision are combined and an end to end method is presented that also does the object detection easily with high accuracy and estimates 3D coordinates of bounding box center that surrounds the observable part of object in cluttered environment image. Proposed network based on U-Net. Classical U-Net architectures is much famous in segmentation of satellite and medical images, but we made some modifications in it that convert the u-net to a detector and mask generator for object in image. In addition, this method can act as a 3D coordinates estimator by reusing the u-net decoder part as a feature extractor and add some fully connected layers to it. proposed network can obtain acceptable results with shorter training times in a small data set and it can useful for navigate the robots. we created a dataset containing 500 pairs of images from 10 objects that are cluttered in each image. the model was evaluated on mentioned dataset and The results was impressive.
Towards Real-Time Illegal Logging Monitoring: Gas-Powered Chainsaw Logging Detection System using K-Nearest Neighbors

Deforestation is exponentially depleting the planet's biodiversity and natural ecosystems at an alarming rate. This research aims to address illegal logging through realtime alerting and monitoring of suspected gas-fueled chainsaw sounds in the forest. Features were extracted from a collated nature sound dataset and trained on a supervised machine learning algorithm. The model is deployed through a microcomputer to process the chainsaw sounds through radio frequency transmission. The system has a desktop application that triggers an alarm and visualizes relevant information from the detected illegal logging activity location. The device prototype is easily-replaceable, modular, and portable and can be reconfigured to large-scale domains such as rainforests. The main contributions of this research are the improvement of alert and monitoring of illegal logging through (1) real-time and online audio analysis and detection of gas-powered chainsaws sounds through k-nearest neighbors; (2) a deployable prototype capable of listening to chainsaw sounds in the forest while buried, and (3) development of a graphical user interface for monitoring of module feedback and responses. The experimental results show that our system has an accuracy of 96.00% an F1-score of 94.34%.
Reliability study of flexible sodium-ion detection sensor

Flexible chemical sensor with high sensitivity and reliability is important for health monitoring applications. A typical sodium-ion detection sensor based on ion selection is made of a reference electrode and a working electrode. The reliability of the electrodes is still challenging to achieve accurate and continuous measurements. Here we use the polydopamine-printed technology and chemical annealing method to prepare the flexible sodium-ion detection sensor that presents enhanced reliability. A sensitivity of 54 mV per decade is achieved for the sensor. In the best case, the reliabilities of the working and the reference electrodes are increased up to 95.3% and 93.9%, respectively for the detection of the sodium-ion concentration. This work provides a method to improve the stability of the flexible ion detection sensors.
Type of Communication between Subpopulations and its Effect on the Multi-objective Search Manager Hybridization Framework

Sharing information about the best searching point through the communication process between subpopulations is an important step in island-based evolutionary algorithms. This sharing process can be carried out via three models: migration, abest and a model uses both prior models. In this paper, the impact of these three communication models is investigated on the Multi-objective Search Manager (MOSM) which is a recently proposed island-based framework for hybridizing different search strategies aiming to enhance the performance of optimization algorithm in solving multi-objective problems.
Caching Placement and Offloading in D2D-Assisted Wireless Networks with In-Band Full Duplex

In this paper, we propose a new approach for maximizing the benefits of device-to-device (D2D) communication with cache memory as well as the caching placement and delivery content. To this end, caching placement, scheduling policies, and in-band full duplex (IBFD) are used to maximize the offloading probability, defined as the probability of obtaining the desired content by user. Each user can obtain its content in a local memory or through a helper cache (node that only has memory capacity) or a D2D communications link. A scheduling policy is used to prevent and control interference from D2D in the distribution method. The mode of choice between Half-Duplex (HD) and IBFD with a fixed probability in device-to-device communication is considered. The results show that our proposed approach, compared to related work, leads to an improvement in the offloading probability.
Speaker Extraction Using Stacked BLSTM Optimized with Frequency-domain Differentiated Spectrum Loss

We propose a novel speaker extraction method that extracts the target speaker from the mixture of the target speaker and an unknown interferer in a semi-supervised scheme. The dynamic nature of the speech signal urges the use of Recurrent Neural Networks (RNN). For this work, we use two stacked layers of Bidirectional Long Short-Term Memory (BLSTM) to account for the continuity between the consecutive speech frames. Moreover, to preserve the spectrum changes of the target speaker in the frequency domain, we propose a loss function that differentiates between the neighboring frequency bins in a frame. This loss function is a weighted sum of the frequency-dependent loss function and Mean Squared Error (MSE). The evaluation results on the Speech Separation Challenge (SSC) dataset show that the proposed approach outperforms the baseline method, which uses a Deep Neural Network (DNN), in terms of three measurement scores such as Speech to Distortion Ratio (SDR), Perceptual Evaluation of Speech Quality (PESQ) and Short-Time Objective Intelligibility (STOI). Also, By comparing the aforementioned evaluation metrics from two similar networks, where one is optimized by the proposed loss function and the other optimized by MSE, we show that the new loss function provides better results for speaker extraction applications.
Shunt Active Power Filter Based on Cross-Switched Multilevel Inverter

Shunt Active Power Filter (APF) is a shunt connected converter which can controlled in a way that the nonlinear load current harmonics on the grid side as well as the reactive power can be compensated. In this research, a shunt active power filter is proposed based on the cross-switched multilevel inverter (CSMLI). Owing to the inherent feature of capacitors voltages self-balancing capability in this multilevel inverter, the capacitors voltage balance is achieved. As the output voltage waveform is a multilevel waveform, the quality of the output waveforms is improved. The employed control system for the proposed APF includes the second-order generalized integrator phase-locked loop (SOGI-PLL) for synchronization with the grid and the proportional-resonant (PR) as the current controller. Using SOGI makes it possible for the APF to operate properly even if the grid voltage is extremely polluted by harmonics. Simulation results on a single-phase 5-level inverter based APF are presented to verify its performance in current harmonics mitigation and reactive power compensation. Also, the voltage balance among capacitors is obtained.
A low-power high-speed two-stage dynamic comparator with a new offset cancellation technique in 90 nm CMOS technology

A new high-speed low-power two-stage dynamic comparator is proposed, using a new offset cancellation technique for the resolution enhancement. The input-referred offset voltage equations are derived, and the mechanism of offset cancellation is analytically explained. The simulation results for the 90 nm CMOS technology demonstrates that the proposed comparator can work up to 4 GHz clock frequency while the delay time and standard deviation in offset voltage are 44.6 ps and $518\ \mu \mathrm{V}$, respectively. The power consumption is $341\ \mu \mathrm{W}$ @ 4 GHz with a 1.2 V supply voltage.
Investigation of Retention Failure Behavior in Analog RRAM Devices

In this paper, we investigated retention failure behavior in analog RRAM devices. The retention failure time was found to be longer in TiW/ Al<inf>2</inf>O<inf>3</inf>/Ta<inf>2</inf>O<inf>5</inf>/Ta device than that of TiW/HfO<inf>2</inf>/Ta<inf>2</inf>O<inf>5</inf>/Ta device. Higher diffusion barrier for oxygen ions/vacancies causes retention time to be longer in a TiW/Al<inf>2</inf>O<inf>3</inf>/Ta<inf>2</inf>O<inf>5</inf>/Ta device. Multiple memory states were observed and found to be stable for at least 10<sup>4</sup> s at room temperature and this failure time is defined as the time for the resistance variation to remain within 10%.
Identification of Malware Families for Creating Generic Signatures : Using Dynamic Analysis and Clustering Methods*

An important step in fighting malware is the creation of generalized signatures for the detection and removal of these malware. Millions of new samples are received in anti-malware research labs every day. Generating signatures for these malware requires many techniques i.e. static and dynamic analysis, reverse engineering and identification of malware families, etc. In this research, we used unsupervised learning methods to identify various malware clusters (families). Once similar malware is clustered together then there is no need for generating unique one-to-one signatures to detect this similar malware. Instead, only a generalized signature is enough to detect most of the malware in a cluster. This approach not only speeds up the detection of malware but also reduces the frequency and volume of signature updates on client-side anti-malware applications. We performed a dynamic analysis of 1247 malware samples of different families in a controlled environment (cuckoo sandbox) and features relevant to classification /clustering were extracted through python scripts (1194 features from each sample). Then machine learning methods for unsupervised learning were trained via these features. K-means, Mini-batch K-means, Agglomerative clustering, spectral clustering, and density-based clustering methods were applied to our dataset and 10 distinct clusters were identified based on best scores. Clustering being a heuristic approach, performed well in this work. Visualization of resulting clusters/groups confirmed the presence of different families. The best score was obtained using K Means and mini-batch K Means with n=10.