Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of true and false news

In the project of false news detection, model which can judge whether the news is fake was built. By using this model, the information safety degree of the society will be strongly improved. To start with the work, a model was created by using the Tensorflow. Then the loss function and optimizer were used to train the model. Finally, a plot of accuracy and loss time was created in order to evaluate the model effectively. After these steps down, a model which can test whether a piece of news is fake will be created. From the process of exploring machine learning, the ability of using Excel to evaluate data was improved, and the developing intelligent recognition with python was achieved.
A Low Power and High Speed CMOS Comparator at Croyogenic Temperature

Cryogenic CMOS (Cryo-CMOS) circuits play an important role in quantum computer application. In readout circuit of quantum signal, Cryo-CMOS Analog to Digital Converter (ADC) is a very significant block. Comparator operated at cryogenic temperature becomes a required circuit. This work proposes a architecture of dynamic comparator based on Semiconductor Manufacturing International Corporation (SMIC) 0. 18μm CMOS technology. This circuit has been simulated in BSIM3v3 model which was revised according to previous work. In 4.2K, the proposed comparator can achieve about 20% reduction of power compared to conventional double dynamic comparator.
Integration of Indoor and Outdoor Voice Based Navigation Detection using Light based Communication (Lifi) & IoT

Global Positioning System [GPS] has always established a tracking application, which is similar to the implementation of actual smartphones. Even though, the existing system holds this feature but on the other hand the door navigation has its own complication because of the utilization of portable devices used in different events. This includes location based application and other features which are becoming a research priority. Whereas in the proposed system there is integration done between the indoor and outdoor navigation by tracking through Wi-Fi technology. Here, for establishing the indoor navigation Wi-Fi is utilized and when the person is out of range of the Wi-Fi network then GPS comes into the range for sharing the location. The modification done in this project provides additional features such as voice based input, which will be useful for the blind for their navigation and this improves the efficiency of the project.
Stochastic Spintronic Neuron with Application to Image Binarization

The hardware implementation of neural network has always been of interest to the researchers as it can significantly increase the efficiency and application of neural networks due to the distributed nature of Artificial Neural Networks (ANNs) in both memory and computation. Direct implementation of ANNs also offer large gains when scaling the network sizes. Stochastic neurons are among the most significant aspects of machine learning algorithms and are very important in different neural networks. In this paper, a hardware model for the stochastic neuron based on the magnetic tunnel junction (MTJ) in subcritical current switching regime is proposed. Functional evaluation of the proposed model demonstrates that the behavior of the proposed model is comparable to the mathematical description of the stochastic neuron, and it has a negligible error in comparison with the theoretical model. The simulation results of image binarization over 10,000 images indicate that the proposed hardware model has only 0.25% pack signal to noise ratio (PSNR) and 0.02% structural similarity (SSIM) variation compared to its software-based counterpart.
IoT Based Smart Energy Forecasting and Data Logging System

The energy crisis is one of the prime challenges being faced by many countries today. To an enormous extent of the demand for energy in industrial development has increased tremendously. The shrewd meter as a critical component for the keen framework is relied upon to give monetary, social, and ecological advantages for different partners. There has been a lot of discussion over the genuine estimations of savvy meters. The solution aspect that will decide the accomplishment of shrewd meters is savvy meter information examinations which manage information obtaining, transmission, preparing, and understanding that carry advantages to all partners. A lot of techniques are suggested like an Energy monitoring and prediction system which is an efficient technique to watch the devices present inside a house or industries and provide notification about their abnormal behavior. The implementation of an IoT-based system for skilled energy administration in Industries and home usage is analyzed. The users can view their status through the IoT-based android application and the web server.
A survey on: Facial Emotion Recognition and Classification

Expression on human faces have been of extreme use for understanding the feeling or emotion of a person and it has consistently been a simple undertaking for people, yet accomplishing a similar assignment with a computer based technique or algorithm is very difficult and testing. There are various algorithms and techniques to analyze how human emotions, which are frequently communicated by face demeanor, could be perceived. This paper provides a comparative study of various strategies and algorithm that have been inspected for detecting emotions on human faces. The study incorporates various forms of literary works and explores led by experts and scientists that involves numerous viewpoints identified on emotion detection. Such a initial framework utilizes a rearranged technique called ‘Viola Jones Face Detection’ for localization of faces and further emotion classification can be achieved using various classifiers. Afterwards ‘Zernike moments’, ‘DCT transform’ or ‘LBP’ can be utilized for extracting the features. Some other technique Utilizes convolutional neural organizations facial emotion recognition. The technique depends on two-section convolutional neural organization (CNN): initially it eliminates the image background, and the subsequent part focuses on the facial component vector extraction. Computer vision has thus paved ways for emotion detection through inspecting human faces through various algorithms and techniques.
Image Denoising Method Based on Hybrid Deep Dictionary Learning and Elastic Threshold

The application of machine vision in the modern industry makes intelligent manufacturing possible. At present, circuit board related fault detection, such as broken wires, missing solder joints, and other problems frequently occur, the manual recognition speed is slow and the false detection rate is high. In response to this problem, this research proposes an image noise reduction processing method that combines deep dictionary learning and elastic thresholds, which can serve computer image processing, thereby greatly improving the detection speed of printed circuit boards, and ultimately serving the processing and production of modern electronic products.
Enhanced Tensor-Based Joint Channel and Symbol Estimation in Dual-Hop MIMO Relaying Systems

In this letter, an enhanced alternating least squares (EALS) algorithm for joint channel and symbol estimation is proposed in a dual-hop multiple input multiple output (MIMO) relaying communication system based on a simplified Khatri-Rao space-time (KRST) coding at the source node. The proposed method exploits the parallel factor (PARAFAC) and PARATUCK2 models at the relay node and the destination node, respectively. Exploiting the structure of these tensor models, we present an iterative alternating estimation scheme using optimized step size factors for accelerated convergence and lower overall complexity compared to conventional approaches. Identifiability conditions and computational complexity are analyzed, and simulation results are provided to corroborate the effectiveness of the proposed algorithm.
Data Life Aware Model Updating Strategy for Stream-Based Online Deep Learning

Many deep learning applications deployed in dynamic environments change over time, in which the training models are supposed to be continuously updated with streaming data to guarantee better descriptions of data trends. However, most state-of-the-art learning frameworks support well in offline training methods while omitting online model updating strategies. In this work, we propose and implement iDlaLayer, a thin middleware layer on top of existing training frameworks that streamlines the support and implementation of online deep learning applications. In pursuit of good model quality and fast data incorporation, we design a Data Life Aware model updating strategy (DLA), which builds training data samples according to contributions of data from different life stages, and considers the training cost consumed in model updating. We evaluate iDlaLayer's performance through simulations and experiments based on TensorflowOnSpark with three representative online learning workloads. Our experimental results demonstrate that iDlaLayer reduces the overall elapsed time of ResNet, DeepFM and PageRank by 11.3, 28.2, and 15.2 percent compared to the periodic update strategy, respectively. It further achieves an average 20 percent decrease in training cost and brings about a 5 percent improvement in model quality against the traditional continuous training method.
Research on International and Domestic Internet of Things Security Policy

With the vigorous development of the Internet of Things (IoT) industry, the cybersecurity issues of the IoT are emerging. Putting the IoT cybersecurity issues into priorities has become a common practice in many countries around the world. In this paper, we expound on the relevant strategies, regulations, standards, guidelines, and other policy documents released by domestic and foreign governments in recent years. Firstly, the paper focuses on the content and characteristics of the IoT security policies of the US, EU, UK, Australia, then summarizes the development of China's IoT security policies. Based on these, the paper gives some suggestions for the development of IoT security in China.
Modeling the Algorithm of Cumulative Sums in the Applied Problems of Detecting the Signals with Random Time of Occurrence in non-Gaussian Noise

The methods of mathematical modeling of the cumulative sums algorithm in the problems of detecting signals with a random moment of occurrence in non-Gaussian noise are considered. The probability of false alarm errors and correct detection of imbalance based on the statistical characteristics of the algorithm of cumulative sums obtained by modeling real processes is calculated. An experimental distribution of the cumulative sum in state of imbalance at a current step for detection without reflecting or absorbing screens is obtained. A linear dependence between the means of the cumulative sum taken in separate sections is determined. Based on modeling real processes, the probability of correct detection and false alarm under the influence of non-Gaussian noise with a bimodal distribution is calculated. Effectiveness of cumulative sums algorithm both consistent and inconsistent with the density of the probability distribution of influencing noise is compared.
Optimal Sensing Task Distribution Algorithm for Mobile Sensor Networks With Agent Cooperation Relationship

We propose a task distribution algorithm based on agent correlation to enhance the comprehensive research on the distribution sensing tasks in mobile sensor networks. First, in the proposed algorithm, the score and feature factors of the mobile agents are considered comprehensively in a direct correlation model, which is constructed by combining the direct and indirect correlation samples. Second, we introduce a mobility model based on the exponential distribution, and obtain a calculation method of probability parameter $\lambda $ according to the analysis presented in this article. Finally, we integrate the constructed correlation model and mobility model; which are then applied to the distribution algorithm. By experimental research, the task distribution algorithm based on relationships of agents we proposed, has a precision improvement of 57.23% over MTPS, which is a fine-grained multitask allocation framework algorithm, and 12.31% of that to sequential MF, which is a recommendation algorithm based on collaborative filtering by exploiting sequential behaviors. These results indicate that the proposed algorithm improves the task distribution performance significantly, and offers a more accurate and reliable service.
Proactive Eavesdropping in UAV Systems Via Trajectory Planning and Power Optimization

Recently, unmanned aerial vehicles (UAVs) have received high significance. With increasing unmanned aerial vehicles UAVs’ wireless communication activities, preventing unauthorized UAVs’ activities becomes a significant development for UAV operation in 5G and beyond 5G (B5G) communication systems. In this paper, we study a scenario where a legitimate UAV monitors suspicious UAVs’ communication via proactive eavesdropping. To achieve the maximum effective eavesdropping rate, a successful algorithm by trajectory planning is first developed. Furthermore, another algorithm is proposed by joint optimizing jamming power and trajectory planning. Numerical results validate that the proposed algorithms achieve significant 88.7% − 154.5% gains in terms of the legitimate effective eavesdropping rate compared to the reference algorithm.
Fingerprint Encryption for Biometrics Security using XOR Logic

Fingerprint biometrics provide higher confidence in the identity verification process and help create user accountability. Fingerprint as one of the most popular types of biometrics and one of the simplest and most valuable security data that can be protected by all secure physical access to the building. There are various types of biometrics such as fingerprints, iris, voice, body, palms, heart rate and the advantages and disadvantages of each type of biometrics are also discussed in this paper. Lots of security devices that rely on fingerprints as identity verification. Therefore, safeguarding identification is required at the time of distribution by disguising it. XOR Logic's security method in fingerprint biometrics can help maintain data security.
Bit Error Rate Performance of Power Domain Non-Orthogonal Multiple Access Systems

Article focuses at research Bit Error Rate (BER) for Orthogonal Frequency-Division Multiple Access (OFDMA) infocommunication system with Power-Domain Non-Orthogonal Multiple Access (PD-NOMA) for two user equipments. Developed imitation model in MATLAB/Simulink environment with Symbol Level - Successive Interference Cancellation (SL-SIC) is presented. Simulation parameters are equivalent of downlink 5G New Radio (5G NR) standard by 3GPP for second numerology with Quadrature Phase Shift Keying (QPSK) and Quadrature Amplitude Modulation (QAM) with order M = 16, 64, 256 on subcarriers. Presented dependencies BER from Signal-to-Noise Ratio (SNR) for different levels mean power of signals on subcarriers for Additive White Gaussian Noise (AWGN) channel. All presented results received for sample size providing confidence level no lower 90% and relative accuracy no more 10%.
Battling the CPU Bottleneck in Apache Parquet to Arrow Conversion Using FPGA

In the domain of big data analytics, the bottleneck of converting storage-focused file formats to in-memory data structures has shifted from the bandwidth of storage to the performance of decoding and decompression software. Two widely used formats for big data storage and in-memory data are Apache Parquet and Apache Arrow, respectively. In order to improve the speed at which data can be loaded from disk to memory, we propose an FPGA accelerator design that converts Parquet files to Arrow in-memory data structures. We describe an extensible, publicly available, free and open-source implementation of the proposed converter that supports various Parquet file configurations. The performance of the converter is measured on an AWS EC2 F1 system and on a POWER9 system using the recently released OpenCAPI interface. A single instance of the converter can reach between 6 and 12 GB/s of end-to-end throughput, and shows up to a threefold improvement over the fastest single-thread CPU implementation. It has a low resource utilization (less than 5% for all types of FPGA resources). This allows scaling out the design to match the bandwidth of the coming generation of accelerator interfaces. The proposed design and implementation can be extended to support more of the many possible Parquet file configurations.
CNN based U-Net with Modified Skip Connections for Colon Polyp Segmentation

Colonoscopy is a medical procedure performed to detect the anomalies in the colon and rectum. A thin flexible wire embedded with the camera is inserted to directly visualize the colon. Direct visualization enables early detection and removal of the polyps in the colon. Polyps exist in different shapes and sizes. The physicians find it very challenging to diagnose small polyps in colonoscopy video. Delay in polyp removal leads to colorectal cancer and leads to cancer-related death. In this work UNET architecture with spatial attention layer is proposed to improve the precision of segmenting polyp regions in colonoscopy video. The CNN models proposed in the literature for polyp segmentation are basically trained using common loss functions such as dice and binary cross entropy loss. Under the training of these loss functions the model learns poorly in segmenting small sized polyps. This can be solved by using focal Tversky loss. Experiments are conducted on a publicly available dataset. Results show that UNET with spatial attention layer trained with focal Tversky loss performs better compared to standard UNet model trained with common loss functions.
Microarray Colon Data Classification using Hybrid Classifiers

The major cause of cancer disease is due to genetic disorders and the Microarray technology has been developed to analyze the expressions for more than thousands of gene samples at a time. The Microarray data is of high dimension and it contains noise that affects the classification accuracy. This paper has applied eight different classifiers to a human colon cancer dataset to analyze the performance of classification. The eight different classifiers namely K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Log Regression based LDA, Exponential LDA, Hybrid LDA, Adaboost, Artificial Bee Colony- Particle Swarm Optimization (ABC-PSO) classifiers are used. In this paper, four types of hybrid classifiers are proposed. Then the existing classifier and hybrid classifiers performance are analyzed. The obtained results illustrate the accuracy of 100% is obtained with Log regression LDA and Hybrid LDA classifiers. The classifier’s advantages are that it produces good classification accuracy and misclassification errors reduction when compared with the other conventional classifiers.
Depression Prevention by Mutual Empathy Training: Using Virtual Reality as a Tool

The increasing number of diagnosed anxiety and depression has been aggravated by the COVID-19 pandemic, in addition to social alienation in modern life. Therapeutic intervention for depression using serious games is gaining popularity in recent years. However, the prevention for depression among people with sub-health status has not gained enough attention, and the avoidance of relapse depression under post-pandemic social alienation is ever more challenging. This paper proposes a novel approach to prevent depression and its recurrence through empathy training using interactive virtual reality game applications. This study explores the possibility that VR games, as an empathy machine, can grow into a social network with interpersonal mutual and empathetic support that benefits both prevention and intervention methods for depression.
Self Healing Memory Using 2D Spares

Test cost and yield enhancements are the vital parameters as the memory capacity and density increase. Built-in redundancy analysis (BIRA) is primarily used for incorporated memories to resolve yield and quality by replacing faulty cells with available cells. Various BIRA approaches need different area overheads to achieve optimal repairs. It is not easy to get low area overhead along with optimal repair rate. A novel BIRA method is proposed that requires less storage for storing faulty address information by deleting the data of unimportant faulty cell addresses. The proposed BIRA examines redundant spares quickly to achieve optimal repair rate. Simulation results show that the proposed BIRA does the spare allocations efficiently.
Recursively Filtered MIMO Transmission With Quadratic Complexity

Wireless millimeter wave base stations need antenna arrays with a large number of elements to achieve a sufficient coverage. This leads to a high computational complexity when digital MIMO transmission schemes are used. The complexity is further increased by the millimeter wave numerologies that reduce the OFDMA symbol times, and by the ultra reliable low latency communication standard for 5G which allows scheduling on a mini-slot basis. To reduce the complexity this letter exploits a filtered buildup of the channel covariance matrix from low rank contributions. A dual application of the matrix inversion lemma transforms the beam weight computation into a formulation that has quadratic instead of cubic complexity in the number of antenna elements of the base station. The filtering restricts the use of the method to slowly varying radio environments.
Automatic Personality Perception Using Autoencoder And Hierarchical Fuzzy Classification

In this research, a study of automatic personality perception based on the Big-five Inventory (BFI) is done. To extract and select appropriate features for the classification, we employ an auto-encoder as a nonlinear feature learning technique. Since an auto-encoder does not extract proper classification lonely, a saddle point is found by a stop criterion based on maximum separate ability in binary classes. The results reveal that nonlinear features enhance the classification results in most personality traits. Furthermore, we use an adaptive neuro-fuzzy inference system classification to model the uncertainty rooted in mental states and affect the classification results through the extracted features. The classification outcomes on SSPNet Speaker Personality dataset demonstrate significant improvement in the results of four traits. These outgrowths verify the existence of uncertainty in the speech signal.
The Specifics of Advertising and PR in the Organization and Conduct of Online Events

The article contains the analysis of the use of advertising and PR-technologies in the organization of online events. This study contains a comparative description of online and offline events in the context of communication support based on the results of participant observation, in-depth interviews and expert surveys, document analysis. It examines advertising and PR technologies that have proven their effectiveness in organizing online the Big Business Forum in November 2020.
[DC] Immersive Journalism - The Future of the News

One intensively discussed solution to audience disengagement in journalism is emotion-inducing immersive journalism (IJ). However, many news users state they turn away from the news due to the high emotionality. This raises the question, whether emotion-inducing IJ is what audiences want. Additionally, it is still unclear, whether IJ has positive effects on a person's citizenship performance. This project fills these gaps and focuses on the value of IJ for both individual citizens and democratic society. Thereby, we provide a nuanced view on the value of emotive technologies for political communication and news production.