Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Collaborative Filtering Recommendation Algorithm Based on Differential Privacy

In this paper, differential privacy protection method is applied to matrix factorization method that used to solve the recommendation problem. For centralized recommendation scenarios, a collaborative filtering recommendation model based on matrix factorization is established, and a matrix factorization mechanism satisfying $\epsilon$-differential privacy is proposed. Firstly, the potential characteristic matrix of users and projects is constructed. Secondly, noise is added to the matrix by the method of target disturbance, which satisfies the differential privacy constraint, then the noise matrix factorization model is obtained. The parameters of the model are obtained by the stochastic gradient descent algorithm. Finally, the differential privacy matrix factorization model is used for score prediction. The effectiveness of the algorithm is evaluated on the public datasets including Movielens and Netflix. The experimental results show that compared with the existing typical recommendation methods, the new matrix factorization method with privacy protection can recommend within a certain range of recommendation accuracy loss while protecting the users' privacy information.
Regression Testing of Massively Multiplayer Online Role-Playing Games

Regression testing aims to check the functionality consistency during software evolution. Although general regression testing has been extensively studied, regression testing in the context of video games, especially Massively Multiplayer Online Role-Playing Games (MMORPGs), is largely untouched so far. One big challenge is that game testing requires a certain level of intelligence in generating suitable action sequences among the huge search space, to accomplish complex tasks in the MMORPG. Existing game testing mainly relies on either the manual playing or manual scripting, which are labor-intensive and time-consuming. Even worse, it is often unable to satisfy the frequent industrial game evolution. The recent process in machine learning brings new opportunities for automatic game playing and testing. In this paper, we propose a reinforcement learning-based regression testing technique that explores differential behaviors between multiple versions of an MMORPGs such that the potential regression bugs could be detected. The preliminary evaluation on real industrial MMORPGs demonstrates the promising of our technique.
Multi-connectivity for URLLC: Performance Comparison of Different Architectures

Ultra reliable low latency communication (URLLC), a use case of 5G, requires high reliability and low end-to-end latency simultaneously. While 5G physical layer enablers like short packet communication and flexible numerology along with one-shot transmission can achieve low latency, multi-connectivity based on macrodiversity is a potential solution for achieving high reliability in single transmission. This paper discusses the various architectures available for implementing macrodiversity based multi-connectivity for achieving URLLC. 3C based packet duplication architecture, diversity combining architecture using maximal ratio combining (MRC) and Coordinated multi-point (CoMP) architecture for interference suppression at cell edge are considered in this paper. Monte-carlo simulations prove that 3C and MRC architecture perform best in terms of reliability and radio resource usage efficiency, respectively.
Questionable suitability of OECD 237 protocol in risk assessment scheme

Persistent xenobiotics are potentially hazardous for the bee larvae despite that they are not directly exposed in contrary to adult foraging bees. The crucial phase of larval development is the first six days after hatching when young larva grows exponentially and during this phase larvae are potentially exposed to xenobiotics via diet. That is why the life cycle of honeybee is still a great challenge for scientists. OECD reflected “this need” and adopted the OECD 237 protocol (Honey bee (Apis mellifera) larval toxicity test, single exposure) on 26th July 2013. The protocol addresses the requirements formulated by the United States, Canada, and Europe to test the toxicity of chemicals compounds on larvae fed with spiked food under laboratory conditions in a tier1 strategy.
Speech Emotion Recognition using Convolution Neural Networks and Deep Stride Convolutional Neural Networks

An assortment of techniques has been presented in the area of Speech Emotion Recognition (SER), where the main focus is to recognize the silent discriminants and useful features of speech signals. These features undergo the process of classification to recognize the specific emotion of a speaker. In recent times, deep learning techniques have emerged as a breakthrough in speech emotion recognition to detect and classify emotions. In this paper, we have modified a recently developed different network architecture of convolutional neural networks, i.e., Deep Stride Convolutional Neural Networks (DSCNN), by taking a smaller number of convolutional layers to increase the computational speed while still maintaining accuracy. Besides, we trained the state-of-art model of CNN and proposed DSCNN on spectrograms generated from the SAVEE speech emotion dataset. For the evaluation process, four emotions angry, happy, neutral, and sad, were considered. Evaluation results show that the proposed architecture DSCNN, with the prediction accuracy of 87.8%, outperforms CNN with 79.4% accuracy.
Prediction of Wind Speed with optimized EMD based RVFLN

In this work, a new hybrid short-term wind speed forecasting approach which is the combination of Empirical mode decomposition (EMD) and Random vector functional link network (RVFLN) is proposed. In the beginning EMD is applied to decompose the chaotic historical wind speed data into number of intrinsic mode function (IMFs). These IMFs are passes through the proposed prediction model called as RVFLN (without weight optimization and with weight optimization (Levenberg-Marquardt algorithm (LMA)). The prediction model performance is tested with thirty minutes and one hour ahead wind speed data. The prediction performance of various prediction models are evaluated using three measure indices (Mean absolute percentage error (MAPE), Root mean square error (RMSE) and mean absolute error (MAE)). It is observed from the result that optimize EMD based RVFL giving better result against the EMD based RVFL without optimization, which proves the efficacy of the presented method for short-term wind speed forecasting. The overall results provided in the result section are satisfactory. Therefore the proposed LMA-EMD-RVFL is a very good forecasting algorithm for real time application in power system.
Intelligent Vehicle Positioning Method Based on GPS/Lidar/Derivative Data Fusion

Ensuring the accurate positioning of the vehicle at any time is the key to navigation and path planning in unmanned driving vehicle. Due to the changes and signal attenuation of vehicle-mounted GPS signals when they pass through electric clouds, and the reflection of GPS signals when they encounter glass curtain wall, and under underground garages, tunnels and overpasses, for example, GPS satellite signals cannot be received by vehicle-mounted GPS locator due to the obstruction of buildings. The above-mentioned situations will cause the vehicle GPS positioning signal to be unstable or even missing. And the current positioning method based on lidar has become the mainstream positioning method for unmanned vehicles. However, under bad weather conditions, the detection capability of infrared waves will be greatly attenuated, and the stability of lidar positioning will also be affected. Therefore, this paper introduces an unmanned driving vehicle positioning method based on GPS/lidar/derivative data fusion when the environment map is known. That is, when the GPS signal is good and the lidar is in good weather conditions, the vehicle can be accurately positioned by GPS/lidar/derivation fusion. While the GPS positioning signal or lidar positioning is unstable and in the event of a short absence, it is still can achieve accurate positioning of intelligent vehicles through a derivation-based approach. The experimental data and MATLAB software simulation results show that the horizontal error of vehicle positioning is within the range of 14.3cm and the longitudinal error is within the range of 8.8cm, within 5 seconds after the lidar and GPS signals are lost simultaneously.
A Model for Assessing the Magnitude and Distribution of Sheath Currents in Medium and High-Voltage Cable Lines

In this article, the authors discuss a simulation model to study the effect of cross-bonding of metallic sheaths, and/or nonmagnetic armors, of single-core medium- and high-voltage cables in the same circuit. In single-core cables, the resistive losses due to the induced circulating currents in cable sheaths or armors cause an increase of the cable temperature, which therefore reduces its ampacity. This is a serious issue affecting the distribution and transmission lines. In addition, the risk of electric shock due to induced voltages may be present if a person is in contact with the armor/sheath at its unbounded end. For these reasons, special bonding techniques of metal sheaths are employed to reduce these currents. The simulation model to assess the magnitude and distribution of induced armor/sheath currents of medium- and high-voltage cables that is herein proposed may be used to optimize the cross-bonding configuration of single-core cables employed in high-current industrial applications or in transmission/distribution power grids. The model has been experimentally validated by means of actual data from a high-voltage underground line and field measurements performed by Prysmian Electronics.
IoT enabled Low power and Wide range WSN platform for environment monitoring application

Widely distributed and versatile nature of the environmental parameters makes it difficult to monitor consistently and continuously. Internet of Things (IoT) enabled wireless sensor networks (WSN) can make all these parameters visible to us and facilitate to understand the behavior of our environment. As both the IoT and WSN are of the distinct layered platform, we need to face technical difficulties while building an environment monitoring system using the available components. This paper proposed an easy to configure, low cost, low-power, and wide-range (LPWR) platform for environment monitoring applications. We implemented the sensor node using most of the available sensors required for environment monitoring application, used LoRa for a low-power and long-range WSN connectivity and the IoT for global visibility. We evaluated the field performance of the proposed platform in terms of sensor data consistency, data delivery success rate of the WSN for LoRa RSSI (Received Signal Strength Indicator), SNR (Signal to Noise Ratio) and the distance between the nodes. We also evaluated the WSN lifetime by measuring the energy consumption of the sensor node.
Entropy Based Image Quality Assessment of Stego Images Created by Pulse Coupled Neural Network

The paper aims to the evaluation of image quality assessments of stego images based on entropy. Two embedding approaches are compared. The first approach is based on a position matrix, which is generated for each image using the Optimized Model of Pulse Coupled Neural Network (OM-PCNN). The second, so called reference approach, is based on generating the random positions for embedding. The subject of research was to observe the increase in entropy of stego images compared to cover images for both embedding approaches. From the point of view of image steganography, a case with zero change in entropy is considered an ideal result. Experiments have shown that the embedding by OM-PCNN position matrix causes smaller increase in entropy compared to the random embedding. Therefore, the OM-PCNN approach is prerequisite for the lower detectability of the message embedding.
Detecting and Tracking Emotions to Identify People Suffering from Prolonged Mental Stress

In today’s fast paced lifestyle, humans are least interested in themselves. Earlier, they used to share their problems and mental trauma with at least someone, but nowadays, they prefer not to share their hardships with anyone. Due to this introvert nature, they feel crushed and sometimes take catastrophic steps such as suicide. We can leverage the ability of Deep Learning algorithms to bridge this communication gap of the 21st century and identify the individuals from a large group who are experiencing mental trauma for a prolonged period of time. Facial expressions are significant in conveying people’s emotion and state of mind. By tracking these emotions over a significant interval, one can identify, understand and help people who might be undergoing some psycho social issues in life. Therefore, this paper aims at developing a system which accurately recognizes and tracks facial expressions in real time with the help of a Convolutional Neural Network(CNN) and provides us with the details of such people.
Compressed Sensing Based Sound Sources Localization with One Microphone in A Room

The Sound source location estimation is usually done using an array of microphones. This kind of arrangement provides an environment where the phase or attenuation of the received signals from various microphones is known, which helps in sound sources localization. In this paper by using a microphone in the room, the room transfer function’s properties are exploiting for localization of sources of sound. Microphone’s position and shape of the room are assumed to be known. Sensing matrix’s limitations and design guidelines are given. On the basis of sparsity, implementation is done in a room in terms of voxels which are immersed by a source. Furthermore, solution’s exciting thing is that the sound source localization is provided with horizontal plane and 3D coordinates in a room.
Simultaneous Gen2 Inventory and Angle of Arrival Measurement of Backscatter Signals with Multiple Commodity SDRs

Simultaneous inventory and angle of arrival (AoA) measurement of UHF RF tags is strongly demanded for item searching in warehouses and offices. Existing high accuracy AoA measurements of RF tags mainly employ a radio comprised of multiple antennas and coherent receivers to produce synchronized multiple IQ streams. Such radio is usually realized as a dedicated hardware and software, which is not suitable for prototyping new ideas. This paper proposes a method to realize simultaneous inventory and AoA measurement with multiple commodity software defined radios (SDRs). In the proposal, the cancellation of mutual phase offsets, an well known problem of multiple commodity SDRs, is done by physically switching the combination of receiving antennas and receivers with an external RF switch and subtracting the two phase differences. This way, the calibration before AoA measurement can be done during the process of phase measurement with no human intervention. The antenna switching is integrated into a SDR Gen2 interrogator developed on a dual RF-ports USRP. The external RF switch is synchronously controlled with Gen2 inventory progress using Gen2 protocol features called action and target. The accuracy of phase offset cancellation was evaluated with wired and wireless experiments with commercial Gen2 RF tags. The experiments revealed that AoA measurement can be simultaneously performed with Gen2 inventory without any intervention achieving less than 3 degree of phase offset cancellation error.
Analysis of Critical Success Factors (CSF) in Enterprise Resource Planning (ERP) Implementation using Extended Technology Acceptance Model (TAM) at Trading and Distribution Company

Enterprise Resource Planning (ERP) is an integrated system that has been used in several companies, including this Trading and Distribution Company. The company has implemented an ERP system at a certain time. The implementation of the ERP system not only broken off until the testing phase of the deployment process but also required some periodical analysis to improve the effectiveness. Lack of human resource skills and the business process of the company could be several major problems. Consequently, a specified analysis is needed n terms of improving the implementation of ERP in a company. The purpose of this research is to analyze the critical success factors (CSF) on ERP implementation in the company with the aim of identifying success factors that can be used to improve the performance system implementation, especially the human resource. Extended Technology Acceptance Model (TAM) was adopted to run the analysis. The research used quantitative data analysis in seeking core problems. Several variables defining CSF of ERP has built based on the TAM model. The result indicates that each variable in the model is a critical factor determining the success of the system's implementation. The result of the study could be exemplary guidance to find the CSF on ERP implementation.
Hidden Markov-based LDA Internet Sensitive Information Text Filtering

We are in an era of rapid development of Internet information [1], generating billions of text every day. Under the test of such a “digital torrent”, how to ensure the ecological security and healthy development of the Internet has become a technical challenge. Rocchio[2] put forward a linear classifier which is a classification algorithm based on linear vector space model. With the development of hardware devices, the machine learning model has become the mainstream. The main training method is linear regression [3], K-Nearest Neighbor[4], Neural Network Model[5] and Support Vector Machine[6]. This paper raises a hidden Markov model based on feature keywords-themes. This is a statistically based approach. We use the Textrank algorithm [7] to extract feature words from a large number of data sets. Using the Apriori algorithm [8] to quantify the implicit relationship between feature words, we can generate a feature word confidence level matrix and establish a HMM-LDA correlation model. According to the text document which can produce an associated state matrix and a probability state transition matrix, we can confirm the conversion probability of the visible state chain. Thus we can filter and identify sensitive Internet information text. (Abstract)
E-SENSE: Design & Development of an IoT based Environment Monitoring System

Recent advancement in Internet of Things has enabled the development of low-cost internet accessible monitoring devices by combining multiple technologies. There are different technical issues for different application areas requiring a multidimensional research effort to address. Energy efficiency, Reliability and scalability are some major challenges and common requirements in the design of any application. This paper presents, an IoT based environmental monitoring system having custom designed energy efficient & configurable sensing device for resource constraint environment, Mobile application & a web application for data monitoring, storage & visualization. Light weight MQTT protocol & power-efficient hardware management strategies are used to address the issue of resource constraint environment. Results are encouraging as the system has been able to reduce transmission overhead upto 64% and improvement of battery life by 33%.
Reactive Electric Spring Application for a Domestic User with Small Photovoltaic Plant

Nowadays, Renewable Energy Sources (RESs) deliver a significant amount of power into the grid. Because of their intermittent nature, the grid power is prone to fluctuate, causing a variation of the supply voltage of the loads. To stabilize the voltage across the critical loads of a user in a simple way, the Electric Spring (ES) concept has been recently proposed. This paper is concerned with an ES that executes the stabilization under the peculiar circumstances of i) a user connected to a prevalently resistive line such as the low-voltage distribution lines in many countries, and ii) an ES exchanging only reactive power. In the paper, the voltage drop induced by a RES power variation is first analyzed to assess the impact of the RES on the supply voltage of the user loads. Afterwards, the reactive ES operation are investigated, setting its working boundaries and presenting its control scheme. The results of Matlab/Simulink simulations verify the effectiveness of the ES action against the RES variability. The work intends to show the potential of reactive ES in a real application where a user equipped with RES is fed by a weak distribution grid.
Performance Enhancement of Wireless Network by Adopting Downlink Non-Orthogonal Multiple Access Technique

Non-orthogonal multiple access (NOMA) has been achieving remarkable support in the surroundings of next generation communication system. The key advantages of this particular scheme includes high data rates, higher spectral and energy efficiency compared to its various orthogonal schemes. The upcoming next generation (5G) wireless networks requires high data rate as well as higher spectral and energy efficiency with very fast development of Internet of Things (IoT). In this paper, power domain based non-orthogonal multiple access (NOMA) scheme we adopted, which have been compared with already existing orthogonal scheme that is orthogonal frequency division multiple access (OFDMA) scheme. The simulation results show that the proposed NOMA scheme achieves much more data rate with higher spectral and energy efficiency as compared with existing technique.
EsharaGAN: An Approach to Generate Disentangle Representation of Sign Language using InfoGAN

EsharaGAN is a Bangla Sign Digit generation model based on Information Maximizing Generative Adversarial Networks (InfoGAN). Augmenting the mutual information between latent variables and observational variables is the fundamental working principle of InfoGAN. This paper focused on generating disentangle representation of Bangla Sign Digit images using a variant of Generative adversarial network InfoGAN. Working on the IsharaLipi dataset, this model consists of 13 layer network architecture—input layer, dense layer, convolutional layer, transpose convolutional layer, activation and batch normalization layer which minimizes the loss function, computation power and generates non distorted images like the real ones. ReLU and Tanh is used as an activation function. This model provides an exceptional result as the inception score of the model is 8.77 which is remarkable for a generation model.
Hand Tracking in Fire Safety - Electric Cabin Fire Simulation

In this paper, we introduce a new version of fire safety application utilizing hand tracking in the electric cabin environment. This is designed for professionals and students and as a hands-on-training solution requires efficient ways to track and trace hand movements. This version is designed for mobile virtual reality glasses (Realmax Qian) with UltraLeap's Leap Motion sensor. Our demonstration shows the potential of these type of devices to be used in safety training in the future.