Reviewerly/exHarmony huggingface dataset:
Research on machine vision technology based detection and tracking of objects on video image

In order to improve the detection and tracking speed of the objects on the video image, shorten the object tracking time, a method of machine vision technology based detection and tracking of the objects on the video image is proposed. The machine vision technology is analyzed and then introduced into the detection and tracking of the objects on the video image. The pixel difference operation is carried out between the video image and its front one to obtain a binary difference video image. The detection of the objects on the video image is completed by adjustment of threshold value. Finally, according to Camshift tracking algorithm flow, the color probability distribution map of the objects on the video image is obtained to complete the object tracking. Experimental results show that the tracking time of this method is significantly shortened compared with the traditional detection and tracking methods.
Real-Time Action Detection Method based on Multi-Scale Spatiotemporal Feature

Spatiotemporal action detection relies on the learning of video spatial and temporal information. The current state-of-the-art convolutional neural network-based action detectors have achieved remarkable results using 2D CNN or 3D CNN architectures. However, due to the complexity of the network structure and spatiotemporal information perception, these methods are usually used in a non-real-time, offline manner. The main challenge of spatiotemporal action detection is to design an effective detection network architecture and effectively perceive the fused spatiotemporal features. Aiming at the above problems, our paper proposes a real-time action detection method based on multi-scale spatiotemporal feature. Aiming at the problem that only 2D or 3D backbone network cannot effectively model spatiotemporal features, we extract spatiotemporal features by multi-branch feature extraction networks respectively. For the lack of descriptiveness of single-scale spatiotemporal features, a multi-scale spatiotemporal feature-aware attention network is proposed to learn long-term temporal dependencies and spatial context information. And the fusion between temporal and spatial features is guided by fusion attention to highlight more discriminative spatiotemporal feature representations. The proposed method achieves 82.59% and 78.30% accuracy on two spatiotemporal action datasets UCF101-24 and JHMDB-21, respectively and reaching 73 frames/s.
Breast Cancer Detection by Data visualization and Feature selection using XG Boost Algorithm

All around the world, the most common type of cancer diagnosed in women is breast cancer. This type of cancer start occurring in glandular tissue which is called lobules or else other parts of breast tissues. It is very important to detect cancer as early as possible. Tumours are of two types cancerous and non-cancerous commonly known as malignant and benign. In this paper, the Wisconsin Breast cancer data set has been used. It is a tabular form of data set. The prime goal is to visualize the data that we have and then select the best features. After getting all the best features will apply all the machine learning algorithms like KNN, Random Forest, Decision Tree, Naive Bayes, SVM, Logistic regression, and XG boost. Classifiers can help us to build a system that will help to detect breast cancer soon in women. XG Boost algorithm outperforms the other algorithms on our selected feature. It gives an accuracy of 98.75%.
Garbage Image Classification Based on Improved Residual Neural Networks

To achieve a better classification accuracy of garbage images, an improved garbage classification algorithm based on ResNet34 and attention mechanisms is proposed. By adding the joint Convolutional Block Attention Module (CBAM) attention mechanism to a ResNet34 network, the model can entirely use the shallow feature information and therefore improves the classification accuracy. Experiments have shown that the proposed model requires fewer parameters and achieved classification performance than previous artificial network models. Moreover, by visualizing the outputs of different layers of the model, it is proved that the method proposed in this paper can locate the target information more accurately.
Deep Multimodal Architecture for Detection of Long Parameter List and Switch Statements using DistilBERT

Code smell detection and refactoring are crucial to sustain quality, reduce complexity and increase the efficiency of a software application. Code smells are observable patterns in the source code of a program that indicate deeper structural issues. Most traditional methods for code smell classification rely exclusively on structural object-oriented metrics and manually-designed heuristics. We propose a novel multimodal deep learning approach that combines structural and semantic information to detect two commonly-encountered code smells: Long Parameter Lists and Switch Statements. The presented architecture applies transfer learning on DistilBERT to generate vector embeddings representing classes and methods concatenated with numerical metrics for joint feature extraction using CNN, to build a complex mapping between the features and predict the output as smelly or non-smelly. Subsequently, to perform a holistic comparative analysis we also implement two multimodal machine learning pipelines, the first employs a sci-kit learn TF-IDF Vectorizer with Random Forest Classifier, and the second merges CNN with Bi-LSTM. Our approach achieves an accuracy of 91.2% as corroborated by experimental evaluation, outperforming the state-of-the-art techniques.
Research on IoT Perception Technology of Renewable Energy Based on Edge Computing

This New type of power system will be given a variety of renewable energy, energy access needs to implement more comprehensive perception, therefore, in order to solve the traditional Internet of Things (IoT) perception research methods in network information perception accuracy of defects under complex conditions, this paper proposes a sense of renewable energy were calculated based on edge and the technical implementation of couplet system comprehensive perception. Firstly, a cloud-edge-end three-layer distributed IoT sensing framework is constructed. Secondly, all kinds of IoT related data of renewable energy are collected and sent to the cloud. Then, RBF-based state monitor and good state baseline are constructed to accurately perceive the IoT of renewable energy. Experimental results show that the proposed model has high accuracy and provides an effective method for IoT sensing of renewable energy.
Balancing Power and Performance: A Multi-Generational Analysis of Enterprise Server BIOS Profiles

There is growing concern over data center energy consumption and the largest energy consumer in a data center is invariably the IT equipment. Power management systems in a server allow for a balanced or energy efficient modes but often there is concern that efficiency mode will reduce performance. This paper profiles multiple generations of server from the last decade, comparing balanced settings against performance settings. We analyse the potential savings available through using a more efficient BIOS setting and given typical server utilisation conclude that there is very little reason to use the maximum performance settings outside exceptional circumstances.
ResNet Based Modulation Pattern Recognition Method in Low SNR

Modulation pattern recognition is an important research direction in communication systems, and plays an important role in electromagnetic spectrum management, communication signal interception and electronic countermeasures. In order to solve the problem of low modulation pattern recognition performance under low signal-to-noise ratio(SNR), a wavelet fusion noise reduction algorithm is proposed. The algorithm uses the Optuna hyperparameter optimization framework to dynamically construct a wavelet basis function and a wavelet threshold search space, and converts the optimal wavelet basis function. and the optimal wavelet threshold range as a parameter, the original IQ signal is denoised and then fused with the IQ signal to form a new data input neural network, which retains more details of the original IQ signal while denoising the signal. The residual neural network(Resnet) obtains a squeeze and excitation transpose one-dimensional ResNet6, and the squeeze and excitation transpose block is added to the one-dimensional residual block to perform secondary noise reduction on the signal, which improves the recognition accuracy of the modulation pattern under low SNR. The effectiveness of the method is verified by extensive experiments under the open source dataset RML2016.10a.
Outsourced and Privacy-Preserving K-means Clustering Scheme for Smart Grid

Currently, the k-means clustering algorithm is generally used to mine the available characteristics from the massive power consumption data, so as to provide high-quality and customized electricity services for grid users. However, these data is sensitive and can be used to speculate on large amounts of private information, such as users living habits. To address these problems, this paper proposes an outsourced and privacy-preserving k-means clustering scheme (OPKM). Firstly, the additive secret sharing technology is used to split user data into two shares, which are sent to two cloud servers. Secondly, secure distance, multiplexer, minimum and division protocols are designed to achieve the secure cluster initialization and secure clustering for k-means algorithm. The experimental results with real electricity dataset show that the clustering accuracy and efficiency of the proposed OPKM scheme is better compared the existing works.
Improved CLDNN Signal Modulation Recognition Based on Feature Fusion

Aiming at the shortcomings of the existing communication signal modulation identification technology methods such as few identification types and low overall identification rate, this paper proposes an improved Convolutional Long and Short-term Deep Neural Network (CLDNN) signal modulation identification based on feature fusion. First, feature extraction is performed by Convolutional Neural Netwoek (CNN), and an attention module is introduced to obtain low-level features of the effective part. Then, after Long Short Term Memory (LSTM) feature extraction, it enters Atrous Apatial Pyramid Pooling (ASPP) for deep feature extraction. Finally, the superposition and fusion of low-level features and deep features are performed to complete feature extraction and mapping. The network can realize automatic extraction of signal features and classification of modulated signal types. Experiments show that the method in this paper can also effectively identify the communication signal category under low signal-to-noise ratio, and the overall recognition rate is over 94%.
Spatio-Temporal Feature Encryption Malicious Traffic Detection via Attention Mechanism

The traditional encrypted malicious traffic detection algorithm via rules and manually extracted features faced the dilemma of low detection accuracy and excessive dependence on experience for extracting features. It made difficult to detect encrypted malicious traffic. Based on this, this paper proposes a 2-layer attention mechanism encrypted malicious traffic detection algorithm via the combination of spatiotemporal features. Specifically, first we use 1D-CNN and BiGRU to extract spatial features in encrypted traffic packets and temporal features between encrypted streams, respectively, which enriches the features of different dimensions. And then, the soft attention mechanism is focus on the encrypted data packets to extract feature. Ultimately, the second layer of soft attention mechanism is used for aggregating malicious features to experiments. The extraction and comparison experiments demonstrate that the encrypted malicious traffic detection model proposed in this paper is better than the existing methods in fine-grained encrypted malicious traffic detection performance.Meanwhile, the effectiveness of each module is proved by its own comparison experiments.
Specific Emitter Identification Based on Joint Wavelet Packet Analysis

Specific emitter identification (SEI) has been recognized as a powerful technique for civil and military communications to distinguish the radio emitters. In particular, the key idea of this technique is analyzing the received signals to extract the intrinsic non-linearities incurred by hardware impairments of radio emitters, in order to classify those radio emitters that the signals originated from. As the modern electromagnetic environment becomes ultra complicated, the signal propagation may experience severe co-channel interference due to the spectrum reuse. However, many previous SEI methods cannot work properly in low signal-to-noise (SNR) regime, thereby leading to significant recognition rate loss. To address this problem, this paper proposes a novel SEI algorithm using joint wavelet packet analysis. The algorithm decomposes the signal using wavelet packet decomposition, and analyzes the joint wavelet packet coefficient matrix to reduce the influence of noise on the effective information. By extracting features of singular value center of gravity, instantaneous frequency distribution and information demission, the specific emitter is identified by support vector machine (SVM) based on voting mechanism. The simulation results show the effectiveness of using we proposed algorithm to extract features under low SNR environment. The recognition rate of the proposed algorithm is over 83% when the SNR is OdB and the proposed algorithm achieves more than 25% improvement over the traditional wavelet analysis algorithm at 0~5dB.
A proposed model for eliminating nonfunctional requirements in Agile Methods using natural language processes

A critical step in the creation of software is the elicitation of requirements. According to most of the research, nonfunctional requirements get less attention from nonfunctional requirements, also necessary for the creation of every new application. A poor choice of elicitation causes the system to malfunction. Without the use of an elicitation approach, the needs and requirements of users cannot be ascertained. Ensuring efficient communication between analysts and users during the elicitation process is the biggest challenge for analysts. This study’s major artifact is a proposed model that creates a conceptual model automatically from a series of agile requirements given as user stories. The accuracy, especially when user stories are succinct assertions that identify the issue to be handled, was one of our case study’s positive outcomes. The objective was to prove that artificial intelligence can be used to elicit software requirements for software systems, and the findings support this claim. An elicitation model for NFR in agile methodology is proposed by this work. The approach will help the software business identify and collect needs for all kinds of software. as well as directing both users and developers in the development of software. Due to the elicitations of both FRs and NFRs of the initial phase in agile projects, which receive less attention, this study decreased the time, effort, and risk.
Hyperparameters Optimization of Convolutional Neural Networks using Evolutionary Algorithms

This paper presents an approach for tuning hyperparameters in Convolutional Neural Networks (CNNs) by adopting evolutionary algorithms, i.e., Genetic Algorithms (GAs). CNNs include abounding hyperparameters that must be adjusted cautiously to accomplish the highest classification accuracy in case of image classification tasks or other related tasks. GAs are used to effectively go across the enormous search space of CNNs hyperparameters and choose the finest CNN architecture that operates well on a given task. CNN architecture contains a sequence of several convolutional layers followed by some fully-connected layers, as the number of layers increases, the number of corresponding hyperparameters will exponentially increase, examples of hyperparameters include but not limited to: number of convolutional filters in each convolutional layer, the number of nodes in the fully-connected layers, and the type of activation function. The proposed tuning framework was tested and evaluated using the SVHN dataset for digit classification of printed digits cropped from pictures of house number plates. The proposed approach can generate a CNN architecture with a validation accuracy of 92.31%.
Path Planning of Mobile Robot Based on Dynamic Chaotic Ant Colony Optimization Algorithm

In this paper, a Dynamic Chaotic Ant Colony Optimization (DCACO) algorithm is proposed to solve the problems of traditional Ant Colony Optimization (ACO) algorithm in mobile robot path planning, such as long time consuming, slow convergence speed and easy to fall into local optimum. In DCACO, cosine annealing strategy is used to improve the expectation heuristic factor to balance the global search ability and convergence speed of the algorithm. In addition, this paper proposes a dynamic chaotic ant colony system, whose core is that improved logistic chaotic operator disturbs pheromone update in the early stage of iteration to avoid falling into local optimization, and is eliminated in the later stage to ensure the convergence speed. The experimental results show that this algorithm is effective and superior in path searching performance and convergence speed compared with the existing state-of-the-art algorithms.
A Hybrid Approach for Auto-Correcting Grammatical Errors Generated by Non-Native Arabic Speakers

Spelling correction is among the most substantial Natural Language Processing (NLP) tasks, which is used as a pre-or post-processing step in many other tasks such as Optical Character Recognition (OCR). Many challenges may face while attempting to implement correctors, including correcting the real-word errors in which the wrong word is one of the language vocabularies; however, its appearance in the context is senseless. The non-native speakers who learn Arabic as a second language might have various spelling errors, especially grammatical errors involving feminine-masculine and definite-indefinite, which are not common among native Arabic speakers; thus, there is a need to employ a spell corrector that can handle such errors however the available spell correctors are not efficient enough to work with their mistakes. The proposed approach employed a rule-based system and the Arabic Bidirectional Encoder Representations from Transformers (AraBERT) model to implement an Arabic grammatical auto-corrector for non-native speakers using the Qatar Arabic Language Bank (QALB) corpus. The corpus comprises 622 sentences containing grammatical and spelling errors generated by non-native speakers. The proposed approach has enhanced the previous works by more than 17%, in which the f1-score was 45.68%. The rules, which are the main contribution, had handled about 30%, and the AraBERT model corrected the rest.
Cross-view information interaction and feedback network for face hallucination

Hallucinating a photo-realistic frontal face image from a low-resolution (LR) non-frontal face image is beneficial for a series of face-related applications. However, previous efforts either focus on super-resolving high-resolution (HR) face images from nearly frontal LR counterparts or frontalizing non-frontal HR faces. It is necessary to address all these challenges jointly for real-world face images in unconstrained environment. In this paper, we develop a novel Cross-view Information Interaction and Feedback Network (CVIFNet), which simultaneously handles the non-frontal LR face image super-resolution (SR) and frontalization in a unified framework and interacts them with each other to further improve their performance. Specifically, the CVIFNet is composed of two feedback sub-networks for frontal and profile face images. Considering the reliable correspondence between frontal and non-frontal face images can be crucial and contribute to face hallucination in a different manner, we design a cross-view information interaction module (CVIM) to aggregate HR representations of different views produced by the SR and frontalization processes to generate finer face hallucination results. Besides, since 3D rendered facial priors contain rich hierarchical features, such as low-level (e.g., sharp edge and illumination) and perception level (e.g., identity) information, we design an identity-preserving consistency loss based on 3D rendered facial priors, which can ensure that the high-frequency details of frontal face hallucination result are consistent with the profile. Extensive experiments demonstrate the effectiveness and advancement of CVIFNet.
Research on the identification method of safety wearing of electric power workers based on deep learning

Aiming at the difficulties of manual monitoring and compliance with the current wear identification of electric power workers, the detection and identification of safety helmets, work clothes, and insulating gloves are used to carry out normative identification and warning, and a deep learning-based power worker safety wear identification method is proposed in this paper. The AlexNet and Inception are introduced to increase the width and depth of the artificial neural network. At the same time, the ReLU activation function with better performance is used to reduce the amount of network computation, and the Global Average Pooling layer is used to replace the fully connected layer with more parameters. The improved convolution neural network model has a total of 13 layers. In order to prevent the network from overfitting, the Early-stopping mechanism and the L2 regularization method are used to improve the performance of the network model. The experimental results show that the algorithm can achieve a good recognition effect on the staff who do not wear safety according to the regulations in the video, and the feasibility and effectiveness of the algorithm in practical application are verified.
Support for students with Special needs during and after the COVID-19 pandemic through E-learning: A Case Study

Faculty in the university's department of special education had a lot of difficulties when it came to providing online learning opportunities for the group of students with special needs during the Corona epidemic. Five academic staff members who work with students that have special needs made up the research sample. To examine current systems and related worldwide studies, a systematic review of the literature was used. The results of the study show that academic staff members have a favorable attitude toward adopting e-learning programs for the group of students who have special needs. Additionally, they aid in these students' ability to manage educational involvement, social connections, and knowledge exchange. The academic staff showed disinterest in investing time in training and agreed that the technique did not call for more time than was often provided for regular class sessions. The academic department's members provided their thoughts on the development of systems and appropriate curricula, which will aid in removing obstacles and promoting support for e-learning for students with special needs for a variety of courses, as the research concluded. The academics' suggestions centered on how to employ e-learning for students with specific needs in an efficient way. Additionally, it was suggested that e-learning be introduced into curriculum, that departments be furnished with the appropriate software and educational resources, and that there be sufficient opportunities for education to enable teachers with special needs to acquire the required abilities. In proposed system, authors would recommend an AI based Chatbot, in higher educational sectors, so that the students who hesitate to come forward to discuss their issues, can also take advantage of support system by interacting and identify some common solutions.