Miracle-dz/UniFAR-FTUs huggingface dataset:
Biometrically Secured ATM Vigilance System

The increase in electronic transactions has contributed to a higher demand for fast and precise identification and authentication of users. In ATM’s, biometric-based authentication offers different benefits. Fingerprints and facial identification are used in biometrical authentication. The drawback of the current ATM authentication method is the use of password-PINs. Since PINs can be tracked and misused quickly. Our planned system is intended to provide greater protection for ATMs to achieve security and to resolve these criminal activities. Here, the PINs are replaced by randomly created OTP that are sent through the IoT. The goal of the work is to fully eradicate the use of ATM cards. The customer will be allowed to continue with the transaction after biometric and OTP pin authentication. The account will be blocked in the event of three consecutive incorrect attempts. This initiative also deals with ATM fraud prevention. In case of any suspicious activity is detected through the vibration sensor results in the closing of ATM doors followed by releasing of the fainting gas and alerting the surroundings. This will catch the perpetrator engaged in the crime and prevent the fraud from taking place.
Prediction of TCS Stock Prices Using Deep Learning Models

A precise forecast is surely critical in stock prediction. Many people are increasingly investing in stocks in recent times; hence it is evident that prediction of stock prices is very crucial. Statistical, Machine Learning and Deep Learning models can be used to forecast Stock prices. In this paper, Statistical method SARIMA is used to predict stock prices. To improve the outcome of forecasting five Deep Learning models namely Single-Layer LSTM, Three-Layer LSTM, Bidirectional LSTM, CNN LSTM and Convolutional LSTM are used. In Single-Layer LSTM and Three-Layer LSTM models LSTM layers are used to give better output. Bidirectional LSTM is used to predict the prices not only based on the previous inputs but also upon the future inputs. CNN is combined with LSTM to capture the underlying abstract features in the input series. Convolutional LSTM is another variation of LSTM which helps to capture the features that contain convolution activity inside the LSTM cell. All the proposed models are validated using evaluation metrics like RMS, RMAE, MAE, R2-SCORE to determine the robustness of the every Deep Learning model used. For this paper, TCS stocks are considered. The proposed methods in this paper can be applied to any other company stocks for the forecasting their stock prices.
RSS-Based Localization of Low-Power IoT Devices Exploiting AoA and Range Information

We present a localization algorithm for low-power long-range Internet-of-things (IoT) networks, which exploits angle of arrival (AoA) and range information from non-coherent received signal strength (RSS) measurements. In this work, each anchor node is equipped with array antennas of known geometry and radiation patterns. The position of the target node and the path-loss exponent to each anchor are unknown and possibly time-varying. The joint estimation problem is formulated with a Bayesian model, where the likelihood functions are derived from the classical path-loss model and an RSS difference model. A message passing method is then exploited for efficient computation of the marginal posterior distribution of each unknown variable. The proposed algorithm is validated using real outdoor measurements from a low-power wide area network based IoT system in a challenging scenario. Results show that the proposed algorithm can adapt to dynamic propagation conditions, and improve the localization accuracy compared to a method that exploits only single geometric feature. Furthermore, the algorithm scales well in different antenna array configurations, and is compatible with various existing IoT standards.
Heart (Pulse Rate) Monitoring using Pulse Rate Sensor, Piezo Electric Sensor and NodeMCU

Traditionally, for monitoring one's health condition, one has to visit the hospitals and it is very hectic work and uncomfortable for every one especially for the patients and their relatives. For life style disease like hypertension and Blood Pressure, one need to daily monitor their health. Hence, monitoring patients remotely is the need of Era, where there is the huge use of ICT in every sector of life. With the use of information and communication technology, the health care systems have produced timely and reliable systems not only in the hospitals, but can be used now in the homes, and even in the workplace of the patients, which in turn is proving to be cost-effective.The main objective of this paper is to build a wireless heart monitoring system, for monitoring patients at any location, any time. This paper has proposed a model for monitoring heart patients using Internet of Things (IoT), which would help them to keep track of their record in real time. In this system, Sensors are used to continuously monitor the patient's pulse rate and resonance frequency. This real-time monitoring of a patient's heart would reduce the chances of heart attack and will save many lives. The data collected from this systems can be used further for the analysis purpose. This paper shows that many short coming of traditional system can be overcome by using such remote ICT based healthcare system.
Coordinated Rate Splitting Multiple Access for Multi-Cell Downlink Networks

This paper considers a two-cell downlink cellular network with two cell-center users and a cell-edge user. By jointly splitting messages of multiple users into a set of common and private parts, we design a new multi-cell transmission scheme, called the coordinated rate-splitting multiple access (C-RSMA), to effectively manage the inter-cell interference as well as the inter-user interference. Under symmetric channel strength and transmit power, the optimal power split ratios among common and private messages are efficiently obtained with respect to maximizing the weighted sum-rate. Finally, it is shown that the achievable rate region of the proposed C-RSMA is larger than that of existing multi-cell transmission schemes relied on orthogonal multiple access and non-orthogonal multiple access.
Control Strategy and Experimental Research of Cable-Driven Lower Limb Rehabilitation Robot

The passive training with fixed trajectory is more suitable for the initial rehabilitation training of patients with no muscle strength in the affected limb. In order to meet the needs of patients’ rehabilitation training in different rehabilitation stages and improve the active participation and rehabilitation training effect of patients, a fuzzy sliding mode variable admittance (FSMVA) controller based on safety evaluation and supervision is proposed for the cable-driven lower limb rehabilitation robot (CDLR) in this paper. The FSMVA controller consists of an inner loop fuzzy sliding mode controller, an outer loop variable admittance controller, and a safety evaluation and supervision module. Through the safety evaluation index of the CDLR system and the comprehensive evaluation of patients’ rehabilitation effect given by rehabilitation physiotherapists, a real-time adjustment algorithm of variable admittance controller parameters is designed, which can realize the real-time switching of training modes and adjustment of variable admittance controller parameters in active training mode and passive training mode. The trajectory tracking experiments with no admittance, fixed admittance, and variable admittance based on safety evaluation and supervision were carried out on the experimental platform. The experimental results show that the proposed FSMVA control strategy has high position control accuracy in the active training mode. In addition, the training intensity and the active participation of patients can be increased according to the patient’s exercise ability. In the passive training mode, according to the training needs of patients, the amount of the trajectory adjustment can be increased to improve the comfort of the training process and the safety of patients. It lays a foundation for the further study of the evaluation system of the rehabilitation training process and the experiment of human-machine interaction compliance.
Application of Machine Learning in Network Security Situational Awareness

Along with the advance of science and technology, informationization society construction is gradually perfect. The development of modern information technology has driven the growth of the entire network spatial data, and network security is a matter of national security. There are several countries included in the national security strategy, with the increase of network space connected point, traditional network security space processing way already cannot adapt to the demand. Machine learning can effectively solve the problem of network security. Around the machine learning technology applied in the field of network security research results, this paper introduces the basic concept of network security situational awareness system, the basic model, and system framework. Based on machine learning, this paper elaborates the network security situation awareness technology, including data mining technology, feature extraction technology and situation prediction technology. Recursive feature elimination, decision tree algorithm, support vector machine, and future research direction in the field of network security situational awareness are also discussed.
Evaluating Machine Learning Algorithms to Detect and Classify DDoS Attacks in IoT

A Distributed Denial of Service (DDoS) attack is a lethal threat to web-based services and applications. These attacks can cripple down these services in no time and deny legitimate users from using these services. The problem has further prevailed with the massive usage of unsecured Internet of Things (IoT) devices across the Internet. Moreover, many existing rule-based detection systems are easily vulnerable to attacks. In this paper, we performed a comparative analysis of Machine Learning (ML) algorithms to detect and classify DDoS attacks. As part of the work, various machine learning algorithms such as Naïve Bayes, J48, Random Forest and ZeroR ML classifiers are compared. Principal Component Analysis (PCA) method has been used to select the optimal number of features. WEKA tool has been used to implement ML algorithms.
Minimum-Variance Importance Sampling Estimator for Fast Simulation of Linear Block Codes over Binary Erasure Channels

In this paper, the problem of efficiently evaluating the error performance of linear block codes over binary erasure channels (BECs) is considered. We propose a novel importance sampling (IS) estimator by deriving the optimal IS distribution of the erasure pattern size. Consequently, a corresponding iterative algorithm for fast simulation purpose is proposed. The effectiveness of the proposed IS algorithm compared to Monte Carlo simulation is demonstrated in estimating the block erasure rate of low-density parity-check codes, and IS gains of almost ten orders of magnitude is achieved.
Proposal of Keyword-Based Information-Centric Delay-Tolerant Network

In this paper, we focus on Information-Centric Delay-Tolerant Network (ICDTN), which incorporates the communication paradigm of Information-Centric Networking (ICN) into Delay-Tolerant Networking (DTN). Conventional ICNs adopt a naming scheme that names the content with the content identifier. However, a past study proposed an alternative naming scheme that describes the name of content with the content descriptor. We believe that, in ICDTN, it is more suitable to utilize the approach using the content descriptor. In this paper, we therefore propose keyword-based ICDTN that resolves content requests and deliveries contents based on keywords, i.e., content descriptor, in the request and response messages.
Theoretical Research On Active Sound Absorption Of 1-3 Piezoelectric Composites Based On Equal Strain Model

Based on the equal strain model of composite materials, the material properties of 1-3 piezoelectric composites are studied, and the active sound absorption property of 1-3 piezoelectric composites is studied by using piezoelectric shunt damping technology. According to the electromechanical relationship of the piezo-composites layer in vibration state and the sound transfer matrix of the medium, the research model is established. In this paper, the relationship between the material properties and the sound absorption property of 1-3 piezoelectric composites is studied, and the active sound absorption property of 1-3 piezoelectric composites is studied by using the negative capacitance and resistance circuit. The results show that: the material parameters of polymer phase in 1-3 piezoelectric composites and the thickness of the composite can affect the sound absorption property of 1-3 piezoelectric composites layer. However, due to the limitation of thickness in practical application, the effect of passive sound absorption of 1-3 piezoelectric composites in low frequency range is worse. The sound absorption property of 1-3 piezoelectric composites can be increased by external negative capacitance and resistance circuit. The absorption coefficient of 1-3 piezoelectric composites layer in low frequency and wide band range can be improved by piezoelectric shunt damping technology.
A survey of RGB-D image semantic segmentation by deep learning

The semantic segmentation task one of a popular difficult problem in computer vision. This task takes great attention to the community of computer vision. Semantic segmentation segments the image which is input into meaningful semantically related regions in another expression, it predicates for each pixel inside image a class label, with appears of depth cameras it provides very useful organized rich information of data depth, RGB-D image not related with illumination when a mixed feature of RGB-D images with depth information features can very improve the accuracy of semantic segmentation. Recently, deep learning satisfied semantic segmentation more easily than traditional methods because of neural networks’ power to automatically learn different representations of proper features. This paper survey provides a background for several techniques work on 2.D RGB-D dataset semantic segmentation in various applications on several different out and door free datasets.
Real Time early Multi Stage Attack Detection

In recent times, attackers are continuously developing advanced techniques for evading security, stealing personal financial data, Intellectual Property (IP) and sensitive information. These attacks often employ multiple attack vectors for gaining initial access to the systems. Analysts are often challenged to identify malware objective, initial attack vectors, attack propagation, evading techniques, protective mechanisms and unseen techniques. Most of these attacks are frequently referred to as Multi stage attacks and pose a grave threat to organizations, individuals and the government. Early multistage attack detection is a crucial measure to counter malware and deactivate it. Most traditional security solutions use signature-based detection, which frequently fails to thwart zero-day attacks. Manual analysis of these samples requires enormous effort for effectively counter exponential growth of malware samples. In this paper, we present a novel approach leveraging Machine Learning and MITRE Adversary Tactic Technique and Common knowledge (ATT&CK) framework for early multistage attack detection in real time. Firstly, we have developed a run-time engine that receives notification while malicious executable is downloaded via browser or a launch of a new process in the system. Upon notification, the engine extracts the features from static executable for learning if the executable is malicious. Secondly, we use the MITRE ATT&CK framework, evolved based on the real-world observations of the cyber attacks, that best describes the multistage attack with respect to the adversary Tactics, Techniques and Procedure (TTP) for detecting the malicious executable as well as predict the stages that the malware executes during the attack. Lastly, we propose a real-time system that combines both these techniques for early multistage attack detection. The proposed model has been tested on 6000 unpacked malware samples and it achieves 98 % accuracy. The other major contribution in this paper is identifying the Windows API calls for each of the adversary techniques based on the MITRE ATT&CK
Issues and Challenges of using Machine Leaning in IoT

An Internet of Things (IoT)-based system is smart system that provides automated and intelligent services. However, its intelligence still depends on the pre-ruled functions, which are only actuated according to rules without any predictions. To improve the intelligence of IoT and to train it to predict the future, it needs to be integrated with Artificial Intelligence (AI) technologies - either Machine Learning (ML) or Deep Learning (DL). Applying AI techniques in IoT involve some challenges and difficulties. In this paper, we go through some of challenges related to both systems of applying ML in IoT, as well as the issues that resulted from applying some of ML techniques in IoT. Moreover, this paper surveyed the background of IoT and ML and their components, and the usage of ML in IoT with possible applications.
Rate Coverage of a Cellular Network with Users Distributed as Poisson Cluster Process

The analytical modeling of cellular networks using stochastic geometry has gone through a major transition with the emergence of Poisson cluster process (PCP)-based random spatial models. The main motivation of using the PCP-based network models is the resemblance with the spatial patterns of the users and small cells in third generation partnership project (3GPP)-compliant industry-level network simulations. Despite the richness in spatial modeling, the analytical framework of the PCP-based models has so far been limited to the characterization of the downlink coverage probability, or the distribution of the signal-to-interference-plus-noise-ratio (SINR) experienced by a user. While the SINR-coverage is indeed a useful first-order performance indicator of the network, the analysis of more sophisticated metrics like the distribution of the downlink data rate, known as the rate coverage, still remains an open problem. This is because of the intractability of the distribution of the cell load or the number of users associated to a base station (BS). In this paper, we make the first attempt towards the derivation of the probability mass function (PMF) of the cell load for the PCP-based network model wherein the users are distributed as a PCP and the BS locations are either independent of or coupled to the user locations. The PMF of the cell load is further leveraged to derive the accurate expressions of the downlink rate coverage.
Posterior Variance Predictions in Sparse Bayesian Learning under Approximate Inference Techniques

Sparse Bayesian Learning (SBL), initially proposed in the Machine Learning (ML) literature, is an efficient and well-studied framework for sparse signal recovery. SBL uses hierarchical Bayes with a decorrelated Gaussian prior in which the variance profile is also to be estimated. This is more sparsity inducing than e.g. a Laplacian prior. However, SBL does not scale with problem dimensions due to the computational complexity associated with the matrix inversion in Linear Mimimum Mean Squared Error (LMMSE) estimation. To address this issue, various low complexity approximate Bayesian inference techniques have been introduced for the LMMSE component, including Variational Bayesian (VB) inference, Space Alternating Variational Estimation (SAVE) or Message Passing (MP) algorithms such as Belief Propagation (BP) or Expectation Propagation (EP) or Approximate MP (AMP). These algorithms may converge to the correct LMMSE estimate. However, in ML we are often also interested in having posterior variance information. SBL via BP or SAVE provides (largely) underestimated variance estimates. AMP style algorithms may provide more accurate variance information. The State Evolution analysis may show convergence of the (sum) MSE to the MMSE value. But we are interested also in the MSE of the individual components. To this end, utilizing the random matrix theory results, we show that in the large system limit, under i.i. d. entries in the measurement matrix, the per component MSE predicted by BP or xAMP converges to the Bayes optimal value.
The Mediating Effect of Positive Illness Cognitions on Experiential Avoidance and Quality of Life in Breast Cancer Patients

Objective: Illness cognition plays an essential role during physical, psychological, and social adjustment among patients with cancer. The present study aims to explore the mediating effects of positive illness cognition on experiential avoidance and quality of life (QOL) in this population. Methods: Between August 2017 and June 2019, we recruited 312 patients with breast cancer in the treatment period from a general tertiary hospital's breast department using convenience sampling. We used the Illness Cognition Questionnaire, the Acceptance and Action Questionnaire II, and the Functional Assessment of Cancer Therapy-Breast Scale. Results: The mean score of QOL was 93.39 (SD: 18.60) for patients with breast cancer. Positive illness cognition was closely related to the QOL and experiential avoidance. Experiential avoidance significantly negatively correlated with QOL (r = −0.59, P < 0.01) and positive illness cognition (r = −0.60, P < 0.01), while positive illness cognition significantly positively correlated with QOL (r = 0.82, P < 0.01). Positive illness cognition had a mediating effect between experiential avoidance and QOL (effect size: −0.56), accounting for 87.14% of the total effect. Conclusions: The QOL was low in Chinese patients with breast cancer. Positive illness cognition had a mediating effect between experiential avoidance and QOL. Caregivers should indirectly improve patients' QOL with breast cancer by improving their positive illness cognition levels.