Miracle-dz/UniFAR-FTUs huggingface dataset:
Rumor Source Detection on Interconnected Social Networks

Social networks are the most widely used platform for spreading information but it is also used to spread false rumor, using source identification we can held the source of spreading rumor responsible, also it can defeat the rumor as well, it has been the area of research for many researchers but due to limitation of different proposed study, it could not be used in the real environment, in this study we propose a methodology that is capable of identifying the rumor source on interconnected network. Interconnected network is considered as the network in which a rumor is propagated from one network to another and identifying the rumor from an independent network does not meet the requirement of identifying the real source. In this study we have proposed a methodology by which we can achieve high performance and accuracy based on an ensemble fusion and maximum voting on different centrality measures. We evaluate our model on two real datasets Facebook and U.S. Power Grid using error distance, this is the first attempt identifying the rumor source on interconnected network but for the acceptability of this model, we evaluate our results with the other approaches of rumor source identification on single network and found satisfactory results and our model outperform LPSI, we also compare our ensemble model with classical centrality based model ecc+clo which is most accurate model till date and found that our model outperforms the classical centrality’s model.
Wearable Foot Pressure and Gait Analysis System

In this paper, the development of an insole foot pressure and gait analysis system has been presented with potential application in diabetic foot ulceration identification and adaptations for orthopedic applications. The system is a low cost, reliable method that emphasizes portability and efficiency. The system employs Force Sensitive Resistors (FSR 402, FSR 406) for pressure sensing and a six degree of freedom gyroscope, MPU 6050 for gait identification. The pressure and the gait pattern so obtained is mapped on to LabVIEW dashboard via wireless communication with ESP8266 Microcontroller.
Circuit Design of Memristor-based GRU and its Applications in SOC Estimation

Gated recurrent unit (GRU) is a variant of recurrent neural network (RNN), which is widely used in applications and tasks related to sequence data processing. However, traditional GRU networks based on von Neumann computing construction, have been facing challenges such as big data dimension and high real-time requirements. Meanwhile, limited internal storage resources and external storage bandwidth have jointly limited the overall performance of hardware implementation of GRU networks. Based on these, a compact scheme for the hardware circuit design of memristor-based GRU network is presented, along with the concrete circuit design of nonlinear activation and the linear matrix operation. The entire scheme is validated by the application of the Lithium ion battery state of charge (SOC) estimation. This work is expected to integrate the neuromorphic electronics and battery management systems, and further promote the development of electric vehicles in smart cities.
Determining the Site Fundamental Period Based on Legendre Spectral Element Wave Simulation

The site fundamental period, or the site fundamental frequency, is a main parameter for site classification in the Japanese seismic design code and the 2021 draft of Eurocode 8. In this study, the site fundamental period has been determined by a spectral element wave simulation based numerical method. First, we use the Legendre spectral elements method and the explicit integration scheme establishing the space and time discrete scheme for a one-dimensional problem. The accuracy and stability of our scheme are verified by comparing with the existing theoretical solutions and the existing literature. Then, we apply the scheme determining the site fundamental period.
Viscous behaviour of dry sand

Tests on air‐dried Hostun sand were performed on a newly developed triaxial apparatus, which allows static and dynamic measurements at ‘Département Génie Civil et Bâtiment’ (DGCB) of ENTPE. The prototype was designed to perform tests in order to investigate the viscous effects of sand from small strain (some 10−5m/m) up to intermediate strain (some 10−3m/m). The accuracy of the measuring system is ensured by the use of local non‐contact displacements transducers and by the use of an internal load cell. The viscous behaviour of sand is experimentally studied for loose and dense specimens for confining pressure values from 80 up to 400 kPa and considering different histories of loading. Three types of viscous loading: creep periods; stress relaxation periods; and paths with stepwise changes in the strain rate are specifically analysed. The proposed viscous modelling is issued from a three‐component general framework and is able to reproduce the experimental observations. More specifically, the peculiar behaviour of sand observed during a stepwise increase or decrease in the strain rate is detailed and modelled by the viscous evanescent (VE) model, developed at DGCB/ENTPE. Based on the three types of viscous tests, a simple relation is finally proposed for the viscous parameters of the VE model. Comparisons between data and simulations validate the approach. Copyright © 2007 John Wiley & Sons, Ltd.
Fruit Detection and Classification Using Computer Vision Techniques

Fruit detection and classification are very important processes that can lead to automation improvements in agricultural and logistical tasks and therefore lead to a better industrialization of this sector. It makes these tasks more efficient since they usually need a lot of human labor. In this paper an algorithm is developed for the detection and classification of fruits using image processing and computer vision techniques. This will be made using a coding software with no external libraries, this way the working mechanisms of the process can be better understood, making it a good educational resource for students that could be interested into learning computer vision.
Tidal Local Gravity Effects Measured by Remote Controlled Pendulums

The gravitational effects of the Moon on the Earth are verifiable in many different natural systems. Some have pronounced and visible effects, like tides, although others are more subtle in nature, like atmospheric tides or crustal tides. In this paper, the orbital period of the Moon is determined by studying one of the more subtle effects of the Earth-Moon-Sun gravitational interaction, the variation of local gravity at the Earth's surface as a function of the position of the Moon in its orbit. For this purpose, we use a set of remote controlled pendulums at different longitudes. The pendulums are part of the constellation displayed by the World Pendulum Alliance project. The spatial distribution of these accurate apparatus allows the experimental study of the variation of gravitational acceleration at the Earth's surface as a function of latitude. In this paper, we show the experimental verification of the variation of the period of the pendulum as a function of the phase of the Moon and, consequently, the determination of the period of the Moon's orbit around the Earth.
Synergizing Symbolic Execution and Fuzzing By Function-level Selective Symbolization

Constraint solving and environment modeling are two challenging problems for symbolic execution. When a program contains non-linear expressions, it is difficult for symbolic execution to explore the program’s whole path space due to the high complexity of the constraint solving for the nonlinear constraints. Besides, when the program uses a third-party library and the source code of the library is not available, the symbolic execution of the program often under-approximates the analysis by concrete execution or over-approximates by introducing new symbolic variables, which may fail to explore the whole path space or introduce false alarms, respectively. This paper proposes FUSE, a framework of synergizing symbolic execution and fuzzing by function-level selective symbolization to tackle these problems. First, FUSE collects the path constraints of each function selectively and introduces symbolic function invocation expressions for the complex or third-party functions. Then, FUSE combines SMT solving and fuzzing to solve the path constraints. We have implemented FUSE on the start-of-theart symbolic execution engine KLEE. The experimental results demonstrate that FUSE effectively and efficiently improves the code coverage. Compared with the state-of-the-art, FUSE achieves 6. 6x speedups for achieving the same code coverage.
An Effective Heart Valve Disorder Classification Technique Using Phonocardiograms

Phonocardiograms (PCG) or heart sounds serve as a preliminary diagnostic tool in identifying cardiovascular disorders due to malfunctioning of heart valves. Healthcare professionals greatly benefit from tools for automatic detection of these disorders as it requires manual auscultation and interpretation that is subjective and whose effectiveness is dependent on one’s medical experience. The proposed technique consists of following stages: extraction of eight features, analysis of variances (ANOVA)-based feature selection, and classification of heart valve disorders using three different types of supervised learning algorithms, namely support vector machines, k-nearest neighbors, and random forests. The proposed technique is evaluated on five different classes of heart valve disorders taken from the GitHub-PCG dataset and achieves an accuracy of 99.50%. The proposed technique is also evaluated on normal/abnormal binary classification on the Physionet/CinC-2016 dataset and achieves 98.67% accuracy. Experimental results demonstrate that the proposed technique is superior than the previous works with comparable results on the GitHub-PCG dataset and a 1.57% increase in accuracy compared to the best previous results on the Physionet/CinC-2016 dataset. Thus, the technique has exceptional performance on both datasets while not requiring heart cycle segmentation before classification unlike existing techniques.
Churn Prediction of Customers in a Retail Business using Exploratory Data Analysis

These days retail stores and supermarkets are rapidly increasing, and they became very high saturated business. Due to its rapid growth, retail sector is facing very serious problems of customer attrition and churns. So, to overcome this problem, the retail stores and supermarkets need to have an effective churn management strategy. Machine learning, and Data Mining can be used by the management to analyze the churning behavior of customers and help them to retain their customers. To do so, this paper executed explorative data analysis and feature engineering on retail store data set. Five different techniques have been applied namely, Logistic Regression, Random Forest, Decision Tree, K nearest neighbors and XGboost, while Precision, Accuracy, AUC, F1-Score and Recall been used to analyze the performance of classification techniques. This study shows that the proposed model can predict the customer churn with an accuracy of 73% and help management to retain their customers. It is demonstrated in the result that the XGboost is the most efficient classifier for this data set which surpassed all other classifiers in all performance evaluation metrics.
ALKAF: An Anonymous Lightweight Key Agreement Framework For Smart Grid Network

The Internet of Things (IoT) has begun the transformation of conventional grid architecture to advance wireless electrical systems, called Smart Grid (SG). Smart Meter (SM) is a key digital device of the smart grid that is used for bidirectional communication among consumers and distribution servers to manage, control, and exchange energy consumption. Nowadays, rapid changes and enhancements in wireless communication are carried out, and hence, security in an IoT device becomes more challenging. In smart grid architecture, authentication and key agreements are the key part to be secured and there are several protocols with different privacy and security features have been developed. But some of them either suffer from privacy and security fault or generates high computation cost. To overcome the observed problems in the existing schemes, we have suggested an Anonymous Lightweight Key Agreement Framework (ALKAF) for the SG network. To optimize the computation cost, the proposed ALKAF is designed based on XOR operation and hash function. In addition, Elliptic Curve Cryptography (ECC) is being used in this model for public and secret key generation. This model is tested against various attacks to prove reliability, robustness, and anonymity. Moreover, the performance analysis of the proposed framework is carried out and it is observed that ALKAF succeeds to improve security with competitive communication and computation costs.
AP Cooperative Mobility Control for Public Mesh Network

In a public mesh network, we propose a control method to move mesh access points (APs) to optimal locations in response to traffic condition changes and evaluate the effectiveness of the move. To adapt the dynamic movement of pedestrian terminals accommodated in the mesh network, we configure a mesh network with in-UAV APs and move the APs with the UAVs. In the calculation to find the optimal location that minimizes the AP's moving distance and maximizes the user traffic accommodation, it is not realistic to find the exact solution in brute force ways. Thus, we propose a heuristic method (DMT method) that iteratively applies a minimum spanning tree method to determine the connectivity of all terminals and a spring balance model to determine the optimal AP locations. The proposed method was proven effective with the evaluation results in which throughput was improved up to 5.21 times compared to that before the move, and was approximately 95% of the exact solution.
Digitalized Academic Exam Evaluation System, Using Deep Learning and Computer Vision

The proposed system has the target of build a platform with the objective of academic exam creation and evaluation with the productivity and easiness in mind, for the teacher o user. This will achieve with the use of tools of deep learning and computer vision. Where with multiple types of methodologies for: investigation, using standard steps; software, implementing agile's frameworks; and artificial intelligence, with steps defined for this purpose. Will be building a web software, who allows feed with one or more academic exams, using answer's sheet and bring the student's scores owned by the teacher or user. With this idea in mind, the system was designed and implemented, where we have a modularized system with this structure: core, for the main evaluation process; backend, server for petitions for the platform and the frontend, the interface that will be used by the teachers. With the system complete, some test and evaluations were doing, with a performance for diary use, with about 84%, a tiny error margin (about 40%) and evaluation time of average less than 15 seconds. With these tests, the idea for building a platform for teachers was achieved and get a tool with good performance, that can be flawlessly used by teachers inside a web browser.
Impact Analysis of Communication Overhead in NoC based DNN Hardware Accelerators

Advanced Artificial Intelligence (AI) systems that process vast feature sets can be designed using Deep Neural Networks (DNN). The popularity and prevalence of DNN based tasks are mainly due to their ability to achieve state of the art accuracy. However, DNN based systems are computationally intensive with heavy on-chip and off-chip data movement demands. Efficient hardware accelerators with systematized interconnection platform support handling this insistent computation and fast communication requirements. With rise in accelerator complexity, it is important to identify and solve communication overheads imposed due to heavy data movement in the system. Need for increased data transfers come as a consequence of complex computations and structure of data storage hierarchy in different DNN models. Multi-core system with underlying Network on Chip (NoC) framework is a promising design choice for implementing DNN based systems with diverse layers. Data movement plays a vital role in deciding performance of such systems. Analysing metrics that affect latency and energy/power efficiency of system is thus crucial in deciding performance enhancement of these DNN inference accelerators. This paper quantifies the communication overheads associated with accelerating different DNN models which use NoC as its basic communication platform.
Perception of United Nations Climate Change Conference in Social Networks

Expanding influence of social media platforms on the internet has led to an increase in online public discourse and perception regarding varied crucial subject matters. Climate change and climate-related news are some of the most significant discourses online and we have established our work on the conversations in online forums. In this paper, we have analysed the online interaction among groups having differing perceptions and opinions regarding the recent United Nations Climate Change Conference(COP26). We have considered both sentiment analysis and stance detection of the posts for our perception analysis using homophily and polarisation. We have proposed a novel metric for determination of homophily score in the different interaction networks extracted from the social media platform Twitter. We observed that interaction in retweet network exhibits higher homophily and polarisation. The quote tweet network and reply-to networks shows less homophily with relatively higher interaction with users having alternate views.
On the Usefulness of Crossover in Search-Based Test Case Generation: An Industrial Report

The crossover operation is an important component of genetic algorithms for test case generation. The usefulness of crossover, however, is not really clear, especially for industrial settings. This research applies EvoSuite, a well-known searchbased test case generator, to both open-source and industrial Java programs, and investigates the impact of the use of crossover on the code coverage achieved by the generated test cases. Our empirical study shows that the usefulness of the crossover operation varies for open-source and industrial code, and that the crossover operation may not necessarily be good, especially for some industrial programs. We further analyze (un)favorable conditions/code features for the use of crossover operations, providing hints for effective test case generation.
Automotive Multi-Chip System with Cache Coherent Interconnect for Pipelined Parallel Applications

Due to the ever increasing computing performance required by Autonomous Driving (AD), the AD systems of high-end vehicles are equipped with multiple automotive SoCs. For chip-to-chip interconnect interface, cache coherent interconnect interface can be a good candidate since it supports shared-memory system by providing a single globally shared address space. With shared-memory system, the entire system can be managed by a single OS or hypervisor. Applications also do not need explicit data transfer and synchronization through I/O devices to communicate across the chip. However, since the interconnect between chips takes much higher latency compared to on-chip interconnect, significant performance degradation can occur when a host chip accesses memory in a remote chip. This is a painful problem of cache coherent Non-Uniform Memory Access (cc-NUMA) systems. This paper proposes an effective cache coherent interconnect architecture and automotive application binding scheme for automotive cc-NUMA system. The experimental results show that the system performance is improved by 22 % on average with the proposed architecture.
Voltage Control of Power System employing Q-Learning based PID Controller

This paper attempts to design an adaptive proportional-integral-derivative (PID) controller employing a Q-learning algorithm for the satisfactory performance of automatic voltage regulator (AVR). The PID controller parameters have been adaptively selected by applying the Q-learning method for ensuring better voltage regulation. The superiority of the developed controller has been studied and shown through analysis with other prevalent algorithms in the literature. Analysis of the result affirms the mastery of the developed adaptive PID controller considering the speed of response, peak overshoot and damping of voltage oscillations.
Remaining useful life prediction of rolling bearing based on ensemble learning

Rolling bearing is a critical component in rotation machines, and its health status plays a vital role in the normal running of the entire machine. It is beneficial to maintain and avoid some unexpected breakdowns for the machine in the operation process by predicting the remaining useful life (RUL) of the bearing. Therefore, an ensemble learning method is proposed to predict the RUL of the bearing, and three deep learning, namely BiGRU-Attention, TCN-Decoder and modified transformer models, are weighted average in the ensemble learning method. The IEEE PHM 2012 Data datasets are taken as an experiment to predict the RUL of the deep groove ball bearing to verify the effectiveness of the proposed method. Results indicate that the proposed ensemble learning method has good prediction effectiveness on RUL of rolling bearing.
Hydroponic Intelligent Portable System(HIPS)

Many crops are being destroyed due to unpredictable weather conditions. Farmers are not able to produce high yields and face substantial financial loss. In rural areas, land used for agricultural purposes is reduced whereas water scarcity and space availability are the main issues in urban areas. So we need an extra effort to produce more amount of food. This paper proposes an Intelligent Indoor hydroponic system that supports anyone to cultivate their food at home. This system can control and monitor plant growth with the Internet of Things (IoT) and Machine learning. Sensors, actuators are used to collect data and take actions to make the hydroponic system ideal. The indoor hydroponic system needs the exact amount of nutrients and light. The proposed system hardware consists of an esp32 microcontroller, raspberry pi, esp32 camera, pH sensor, dht11 sensor, water level sensor, and grow light. The system continuously monitors all sensor values and updates data into the cloud using API(Application programming interface). Data from the sensors will be analysed, and actuators will act to stabilize the system. Full-spectrum Grow lights give plants the energy they are getting from sunlight. The proposed system can control the light according to sensor data with machine learning, and the system can attain exact growth and high yield with minimal power usage. A dedicated mobile app will be available to users to view the status and insights of the system at any time.