Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Chinese Movie Reviews Based on Latent Dirichlet Allocation Topic Model

With the rapid development of the Internet, information acquisition has become more accessible, and a large number of online movie reviews cover the characteristics of audience preferences in the movie consumption market. Taking the top 10 popular movies in the Chinese box office as an example, this paper collects hot reviews, counts word frequency and visualizes the word cloud to show the keywords. At the same time, this paper uses the topic model to summarize key topics, so that we can gain insight into the hot spots. Finally, we conclude that the audience's attention mainly focus on the movie's theme, plot content, scene design, editing technology, character shaping, etc. Directors and actors have received a lot of attention, and the attention of domestic movies has increased significantly.
Detecting Android Malware Based on Dynamic Feature Sequence and Attention Mechanism

The mechanism of running software on virtual machines partly ensures the security of Android system. However, with all kinds of malicious codes being developed, there has been a huge number of massive security incidents caused by malware on Android. Malware has various code patterns, but their behaviors are measurable. In this paper, a new method of detecting Android malware by analyzing malware's behaviors is proposed. The method is characterized by the ability to mine the contextual relationships between system calls and network activities. Besides, the method requires only a small data set to achieve good classification performance. We propose a set of methods for automatically collecting and organizing dynamic features from Android application Based on the collected features, deep neural network is used to classify software samples. We validate the effectiveness of the proposed method on a set of 2210 applications obtained from Androzoo. The experimental results demonstrate that the proposed method has high detection accuracy against wild malware as compared with other methods. We propose a set of methods for automatically collecting and organizing dynamic features from Android application Based on the collected features, deep neural network is used to classify software samples. We validate the effectiveness of the proposed method on a set of 2210 applications obtained from Androzoo. The experimental results demonstrate that the proposed method has high detection accuracy against wild malware as compared with other methods.
Pixel-level Visual and Thermal Images Fusion Using Maximum and Minimum Value Selection Strategy

In this paper, an effective pixel-level fusion algorithm is presented for visible and thermal images obtained from different sensors. The fused image may contain more essential information for human as well as machine intuition. Because of the said merits, pixel-level image fusion plays an essential role in military, remote sensing, and night vision applications. Initially, a center sliding window is used in the infrared image. The initial feature map is obtained by utilizing the maximum selection strategy between the pixel-intensity of visible image and block-based average of center sliding window. This initial feature map is not able to preserve the useful information, which is applicable for human visible intuition. So in the next stage, the source images are compared by using the minimum selection strategy to obtain the final feature map. Eventually, the fused image is achieved by utilizing the weighted-average technique among the feature maps. To estimate the efficiency of the proposed algorithm quantitatively, and qualitatively tests were carried out on a publicly available “TNO-database”. The experimental results of the proposed algorithm attained higher accuracy as compared to seven existing techniques in visual quality and objective assessment.
Plant Leaf Recognition: Comparing Contour-Based and Region-Based Feature Extraction

Plants play a vital role in the environment. Identifying them and classifying them is an important task for botanists. This study briefly points out- how to recognize plant species using image processing techniques that can help botanists and scientists, the appropriate features for plant species recognition in feature extraction, how can a classification help to increase the accuracy of the plant leaf classification. There are four major phases used in here for the recognition, and they are image input, image pre-processing, feature extraction, and SVM classification. This automatic recognition system is developed using python with Jupyter Notebook environment gives higher accuracy for the plant recognition for the botanists and comparing the feature extractions such as Contour-based and Region-based to get down more accurate results than previous researches is the main purpose of the proposed study. Contour-based and Region-based features were calculated through equations. SVM classification is used for both feature extraction methods. For individual feature extraction the Contour-based feature extraction is more efficient with 72.25% accuracy than Region-based feature extraction with 70.41% accuracy, and for combining both feature extraction SVM classification gives 68.58% accuracy. Contour-based feature is the most appropriate feature for a plant species recognition.
Design Of Satellite Launch Center Network Architecture Base On SDN & NFV

The Aerospace Service Network (ASN) of satellite launch center carries many services, such as images, voice, test data. With the increasing user of ASN, there are more and more equipment connected to the ASN. The complexity of ASN is getting higher and higher. It is more difficulty to maintenance than before. At present, the next generation network technology, such as SDNV NFV, has been widely used in civilian areas. Based on the analysis of existing requirements of ASN, we researched the architecture of next generation ASN. It provides technical support for the construction of the follow-up satellite launch center network.
Real-Time Speech Emotion Analysis for Smart Home Assistants

Artificial Intelligence (AI) based Speech Emotion Recognition (SER) has been widely used in the consumer field for control of smart home personal assistants, with many such devices on the market. However, with the increase in computational power, connectivity, and the need to enable people to live in the home for longer though the use of technology, then smart home assistants that could detect human emotion will improve the communication between a user and the assistant enabling the assistant of offer more productive feedback. Thus, the aim of this work is to analyze emotional states in speech and propose a suitable method considering performance verses complexity for deployment in Consumer Electronics home products, and to present a practical live demonstration of the research. In this article, a comprehensive approach has been introduced for the human speech-based emotion analysis. The 1-D convolutional neural network (CNN) has been implemented to learn and classify the emotions associated with human speech. The paper has been implemented on the standard datasets (emotion classification) Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and Toronto Emotional Speech Set database (TESS) (Young and Old). The proposed approach gives 90.48%, 95.79% and 94.47% classification accuracies in the aforementioned datasets. We conclude that the 1-D CNN classification models used in speaker-independent experiments are highly effective in the automatic prediction of emotion and are ideal for deployment in smart home assistants to detect emotion.
D2D-Enabled Reliable Data Collection for Mobile Crowd Sensing

With increasing more powerful sensing capacities of mobile devices, the Mobile Crowd Sensing (MCS) system requires to collect larger sensing data from participants. Nevertheless, collecting such large volume of data will cost a lot for participants, base stations and MCS server. Even worse, some sensing data cannot satisfy the MCS sensing requirement due to the low quality and are filtered by the MCS server in clouds. Inspired by the D2D technique, where mobile devices can communicate directly with the help of the nearby base station, in 5G networks, we propose the Reliable Data Collection (RDC) algorithm to validate the generated sensing data at device sides in this paper. To be specific, the whole progress is formulated as a Probability problem of Discovering Reliable sensing data (PDR) at client sides, and Expectation Maximization (EM) is leveraged to devise the algorithm. Finally, the extensive simulations and real-world use case are conducted to evaluate the performance of RDC algorithm, and the result shows that RDC outperforms the other two benchmarks in estimating accuracy and saving data collection cost.
Intention to use Governmental Micro-Video in the Pandemic of Covid-19: An Empirical Study of Governmental Tik Tok in China

Micro-video is one of the fastest-growing patterns in the e-government system. It becomes a key tool for the relationship between government and citizens in the digital age. The governments from lower to upper levels launch their own official accounts abundantly and actively to show their work interestingly. Based on the Technology Acceptance Model and Theory of Planned Behavior, this study empirically tests which factors can greatly influence citizens' intentions to adopt governmental micro-video during the pandemic of Covid-19. The results show that PEU, PU, AT, SN, and PBC can positively affect intention to adopt governmental micro-video. Policy suggestions are given based on the findings of this research.
Finding the Key Factors for Movie Influence Using Machine Learning and Sentiment Analysis

This paper aims at examining the key factors for a movie to be influential. Instead of employing the widely tested variables such as box office, the paper quantifies the level of influence by the number of times a movie being referred by other movies. This model was initially proposed by Bioglio and Pensa (2018), and refined in the paper to offset the time impact. Meanwhile, a few factors are selected to verify their relationship with the influence of movies, including movie metadata, sentiments of movie reviews and sentiment fluctuation of movie descriptions. Especially, a Long Short-Term Memory (LSTM) network is built to perform sentiment analysis on movie reviews, and an ensemble model combining rule-based method and LSTM model is also raised. Both results of two models are compared in regression analysis and it is found that the combination method performs better in tree models and LSTM model works better for other regression models. In addition, a novel method of quantifying the level of sentiment fluctuation is as well proposed in the paper. Finally, it is indicated that a movie should probably aim at achieving a higher popularity in the market than a higher reputation among the audience to be influential.
sEMG Recognition Based on Multi-channel Weight Configuration

In order to accurately obtain muscle motion trajectories and corresponding actions, high density multi-channel signal is required to collect Surface Electromyography (sEMG) for analysis. However, during the process of acquisition, the loose contact between acquisition electrode and muscle will decrease of Signal-to-Noise Ratio (SNR), which will affect the accuracy of recognition. So, this paper firstly samples the 64-channel sEMG signal. Secondly, we normalize the result of SNR in each channel as the sample weight. Thirdly, the wavelet transformation is adopted to extract the feature. Finally, we optimize Support Vector Machine (SVM) with the Particle Swarm Optimization (PSO) to improve recognition accuracy. The recognition accuracy is increased about 2.05%, which shows the better performance than the traditional testing strategy.
Does Domain-Driven Design Lead to Finding the Optimal Modularity of a Microservice?

Information systems are moving into the cloud. The new requirements enforced by cloud standards are high availability, high scalability, and a reduced mean time to recovery. Due to these new requirements, information system architecture styles are also evolving. Microservice architecture is becoming the de facto standard for developing highly modular cloud information systems. Since microservices were introduced, there has been an ongoing debate concerning how to choose the granularity of a microservice. In this study, the optimal point of granularity for microservices is examined based on coupling and cohesion values. The present study is based on two design examples generated in previous studies that applied domain-driven design in proposing microservices. Both examples are modified to generate more and less granular microservices. The coupling and cohesion values of the original examples are compared to those of the more and less granular microservices. We observe that domain-driven design has delivered a good end result for finding modular microservices.
Research on Malware Variant Detection Method Based on Deep Neural Network

To deal with the increasingly serious threat of industrial information malicious code, the simulations and characteristics of the domestic security and controllable operating system and office software were implemented in the virtual sandbox environment based on virtualization technology in this study. Firstly, the serialization detection scheme based on the convolution neural network algorithm was improved. Then, the API sequence was modeled and analyzed by the improved convolution neural network algorithm to excavate more local related information of variant sequences. Finally the variant detection of malicious code was realized. Results showed that this improved method had higher efficiency and accuracy for a large number of malicious code detection, and could be applied to the malicious code detection in security and controllable operating system.
Community Overlapping Detection Algorithm based on Bias-Subordination

The real-world network structure often presents the characteristics of overlapping communities. High-quality communities are helpful to understand the real complex networks. The discovery of overlapping communities has become a hot spot in current recommendation research algorithms. Regarding the randomness and instability problems in the original overlapping community discovery algorithm, this paper proposes the overlapping community discovery algorithm CODA-BS, which is based on the priority of community degree subordination. In each iteration process, the CODA-BS algorithm uses information entropy to calculate the vertex threshold, uses the weighted function proposed in this paper to optimize the label membership coefficient, and performs label screening and normalization of the membership coefficient according to the label propagation rules to detect a more ideal community. Tests on the benchmark data set and data sets of Chinese Association for Science and Technology scholars, show that the CODA-BS algorithm has higher accuracy and improved stability than COPRA algorithm, and the CODA-BS algorithm is more accurate and more stable than the classic COPRA algorithm.
A Partial-Lifting-Based Compiling Concolic Execution Approach

It has been a common phenomenon that embedded devices are used ubiquitously while the security of proprietary software running on devices has become more and more significant. These softwares are often presented to researchers in binary forms. Due to the lack of program semantics and meta information, it is very difficult to analyze manually in large-scale binary programs. Therefore, automatic binary program analysis has become a critical research direction. Symbolic execution is an important method of automatic analysis, due to the problem of path explosion in traditional symbolic execution, it is sometimes complecated to explore the deep-level path of the program, and the most of current execution engines using IR translation execution, resulting in symbolic execution velocity has been exceedingly restrained, comparing to native execution. To this end, we propose a binary program oriented partial-lifting-based compile concolic execution approach, which is combined with static analysis to dissect the binary parts that researchers are intrigued in. The program is simultaneously combined with semantic lifting, dynamic memory monitoring, and symbolic compiler to reconstruct it into a symbolic binary program. Users can perform concolic execution on this program repeatly, so as to explore the part of the original binary program at a rate close to native execution.
Sales Forecast of Manufacturing Companies using Machine Learning navigating the Pandemic like COVID-19

This project is all about prediction of turnover of a company using machine learning. From this machine learning model, it can easily predict the next outcome in terms of turnover or whichever is equivalent to it. Future result prediction is very helpful for better understanding of market trends and stocks by using simple machine learning techniques. For this particular project, an Indian automobile industry has been selected for car sales prediction in the era of covid-19. This project shows, in the tough time of covid-19, if every other thing remains constant, what will be the sales trend for various automobile companies. will the graph go downwards or upwards? By using various machine learning techniques, it can predict the trend of graphs and in this project, it has been tried to show like this.
Computer Data Processing Mode in the era of Big Data: from Feature Analysis to Comprehensive Mining

Computer data processing mode in the era of big data from feature analysis to comprehensive mining is discussed in this paper. The integrated detection framework divides the structure of the system operation mode when a large amount of data intrusion attacks the computer in the network environment, and each functional structure is then directly connected to a corresponding system component module. The primary novelty of the paper is through the core effective use of the dynamic reconfigurable features of FPGA, the matching characteristics and matching speed of regular expressions are improved. The feature extraction algorithm is optimized and enhanced for the complexity testing. The experiment results shows that the rate of accuracy has been improved.
A Comprehensive Survey on Implementation of Image Processing Algorithms using FPGA

Image processing is an unceasingly growing area with a range of applications including cryptography, medicine, video surveillance, remote sensing, and many more. Implementing sophisticated algorithms to process the large amount of data using software solutions makes the response slower, and that's where hardware implementation comes into the picture. Field Programmable Gate Arrays (FPGAs) are getting popular due to low latency, connectivity, parallel computing, and flexibility. The unique architecture of the FPGA has made it possible to use the technology for applying in many applications to have better and faster results. This paper is aiming at providing a comprehensive survey on the hardware implementations of image processing algorithms to facilitate the improvement in efficiency using FPGAs. The widely used Xilinx PYNQ is also presented in this paper as they play a major role in reducing the development time.
Analysis of Different CNN Architectures for Tomato Leaf Disease Classification

In the field of agriculture disease in crops are major problem. The main aim of the work is to classify disease in tomato leaf using different Convolution Neural Network architectures. The disease in tomato leaf can affect quality and quantity of production. To overcome this problem early disease identification, classification and detection is required. Recently, deep learning is very popular object recognition and detection. Convolution Neural Network id part of deep learning which is widely used in object detection part. In these different architectures of Convolution Neural Network are used to classify 10 classes of tomato disease. This paper used AlexNet, VGG-16, GoogleNet, DenseNet-121, and ResNet-lOl for classification of tomato leaves. The dataset used is publicly available PlantVillage dataset. DenseNet-121, VGG16 and ResNet-lOl performs almost equal in terms of accuracy, precision, Fl-score, and Recall. Size of DenseNet-121 is only 89.6MB which is very smaller than ResNet-lOl and all other models.
Generation of high voltage nanosecond pulses using Pulse Sharpening switch

The paper exhibits the simulation and experimental results of Pulse sharpening switch along with a 250 kV High Voltage Pulse Transformer for generation of high voltage fast risetime short duration pulse. The Pulse transformer is employed to charge the Pulse forming line integrated with Pulse sharpening switch. The Pulse sharpening switch is a two electrode fast spark gap switch. Fast risetime short duration pulse depends upon the fast closure of Pulse sharpening switch. The switch is pressurized at 3.5 bar with sulphur hexafluoride insulating gas and the inter electrode gap of the Pulse sharpening switch is 2mm. The closure of the Pulse sharpening switch delivers the output voltage of 125 kV, 1 nanosecond rise time with 70 ohm load.
Design and Fabrication of a Lead Zirconate Titanate (PZT) Thin Film Acoustic Sensor

Piezoelectric MEMS acoustic sensors have potential applications in a wide variety of applications including hearing aids, surveillance, heart monitoring, etc. For each of these systems and many others, the acoustic sensors must be miniaturized and have low power requirements. A piezoelectric based microphone can provide a solution to these requirements, as they offer the ability to passively sense without the power requirements of condenser or piezoresistive microphone counterparts. This research effort reports on the design and fabrication of a piezoelectric PZT based acoustic sensor. A circular clamped membrane consisting of a dielectric for structural support and a piezoelectric actuator has been fabricated on a silicon wafer using silicon deep reactive ion etching (DRIE). Sensors ranging from 500–2000 microns in diameter have been fabricated and characterized using scanning laser Doppler vibrometry and calibrated acoustic tone sources. The PZT sensors exhibited a sensitivity of 97.9–920 nV/Pa depending on geometry.