Reviewerly/exHarmony huggingface dataset:
Real-Time Human Emotion Recognition Using Transfer Learning

The research community has been interested in facial emotion recognition (FER) due to its potential uses. The main task of FER in real-time is mapping distinct face expressions to the corresponding states of emotional. The two main phases of the traditional FER are feature extraction and emotion classification. Due to its built-in feature extraction process from images, Deep Neural Networks “DNN”, particularly a Convolutional Neural Network “CNN”, are being extensively employed in FER. This paper proposed a Deep CNN (DCNN) modeling approach based on the Transfer Learning (TL) for developing a highly precise FER system in real-time in which the fully connected layer is fine-tuned to seven emotion states with using k-nearest neighbor (KNN) and support vector machine (SVM) classifiers instead of SoftMax. The suggested FER system has been validated using the ResNet-101 model and SAVEE image datasets. When used with KNN and SVM classifiers for emotion recognition, the suggested ResNet101 model with SVM classifier model obtains 98.41 % accuracy with respect to other performance measures.
Heterogeneous Network Recommendation Algorithm Based on Graph Convolution

There are several types of objects and interaction relations in recommendation systems. However, most graph convolution models consider the network merely as a bipartite graph for convolution, as a result of which, the qualities of nodes in heterogeneous networks are underestimated under different interactions. Therefore, the present study aims at proposing a graph convolution recommendation algorithm for heterogeneous networks. The node interaction in the network were divided into two types of interactions, i.e., the homogeneous interaction and the heterogeneous one. Moreover, attention mechanism was used to extract the node features in the homogeneous interaction, which was subsequently integrated into the node features generated by the heterogeneous interaction convolution. Finally, to generate the prediction score, a multi-layer perceptron was used. Hence, the obtained results corroborated the better performance of the proposed algorithm.
A Fresh Way Targeted on Gear Malfunction Detection

As the most common components in the transmission system of machine, gears play an extremely vital part in it. However, it is precisely because of the key of the gear that the failure of the gear in the mechanical system has become an vital part that cannot be ignored. Gear failures may occur due to production quality, transmission maintenance, or inevitable use and wear. When a gear failure occurs, the specific cause of the failure is unpredictable and occurs randomly. In actual engineering, it is not easy for us to check and determine the failure type of the gear in time. Therefore, an optimization way is urgently needed to timely detect and judge the malfunctions of gears in actual production. It can be known from practical experience that the malfunction of the gear will cause the system to show a unique vibration signal, which can be used to judge the malfunction of the gear. Thus, this paper simulates the occurrence of malfunction signals on the basis of finite element modeling to generate test samples for extreme learning machine training. The experimental results show that the fresh gear malfunction detection way is feasible and achieves the expected accuracy.
A Time Variant Fluid Model for DCQCN Congestion Control Protocol

The development of datacenter applications leads to the need for high throughput and ultra-low latency from the network. Remote Direct Memory Access (RDMA), which is becoming prevalent in datacenter networks in recent years, is capable to provide ultra-low latency and high throughput with less CPU utilization. Congestion control is of great importance for RDMA for its lossless feature. Datacenter QCN (DCQCN) is the most commonly used congestion control mechanism and is deployed in large-scale datacenter networks. To ensure the stability and performance of DCQCN, a fluid model is essential. However, due to neglecting time variant factors in the queue dynamic, the existing fluid model is not precise enough. In this paper, we propose a time variant fluid model which can be more effective to evaluate the stability and performance of DCQCN. In this model, the variation of the queue delay and system cut-off frequency is considered. As a result, the non-monotonic stability behavior is eliminated and a more reasonable and accurate evaluation of the congestion control system is acquired.
A Evolutionary Behavior Tree AI for Neural MMO Challenge

The Neural MMO Challenge aims to study robustness and teamwork in large-scale multi-agent environments. AI needs to explore, search and fight in large-scale environments, and get higher scores than other competitors. Therefore, we developed AI based on evolutionary behavior tree, which has the advantages of strong interpretability, low coupling between modules and strong robustness. Specifically, the AI adopts the idea of divide and conquer, divides the decision-making into two levels: team and individual, divides the four task achievements into several subtasks, finds appropriate agents for each subtask according to the current advantages, and then completes specific actions such as attack, foraging, avoidance and collaboration, and optimizes the parameters with evolutionary strategy algorithm.AI won the silver medal in IJCAI 2022 Neural MMO Challenge.
Structure Recognition for Object Detection and Tracking Based on Star Catalogue

When the queue constituted by warcrafts and ships are deployed, they often move in fixed formation, which like the structure of galaxies in the universe. This work proposes a structure recognition like star pattern matching. It connects each of the three targets into a triangle, then get the angle and sides from the point we interest. In the next frame, triangle similarity will be used to match the structure from previous "star catalogue". This work changes the pixel-level feature of image into a simple "star catalogue". The star catalogue provides a huge help to detection and tracking in fixed formation because it records key information between different object. In the experiment, the method has achieved high accuracy with its characteristics of light weight and fast.
Wavelength-Dependent Image Defogging Method Based on Mie Scattering of Fog Droplets

In this paper, we present an image defogging method based on the physical process of fog altering the light passing through it, in order to restore information in degraded photos from an optical standpoint. To calculate the fog scattering components quantitatively for different wavelength ranges, we integrate the fog droplet distribution functions with Mie scattering theory. To simulate a real fog scene, the method calculates the environmental visibility by first using a droplet distribution function to determine the corresponding fog droplet size, then calculating the Mie scattering efficiency factors using the obtained droplet size, and finally determining the light attenuation coefficient in different color channels. Experiments reveal that this wavelength-dependent strategy achieves outstanding information entropy and a high proportion of visible edges while also having advantages in retrieving items concealed by dense fog.
Dataset Outlier Detection Method Based on Random Forest Algorithm

Outlier detection plays a very important role in real life, and requires long-term and continuous study and research in this field. The purpose of this paper is to study outlier detection methods for datasets based on the random forest algorithm. This paper briefly describes the research background and significance of the field of outlier detection, the research status at home and abroad, the application of outlier detection in various real-world scenarios, and some research problems that need to be solved urgently. The concept of outliers is summarized, and random forests and locality-sensitive hashing algorithms are briefly introduced. The RHSForest algorithm is proposed, the idea and process of the algorithm are discussed in detail, and the parameter settings and evaluation indicators are discussed in detail. Then the RHSForest algorithm is verified and evaluated by experiments. The experimental results are then analyzed, and the experimental results on 5 benchmark datasets show that the RHSForest algorithm has an AUC value of up to 95% in the Glass dataset, providing consistent performance improvements for the detection of outliers.
Search-based Motion Planning Approaches for Two-link Arms

Over the past decade, motion planning in robotics has become not only a popular research field in academia, but also one of the most applicable technologies in various industries. There are two famously known approaches to tackling this problem, namely, the search-based approach and the sampled-based approach, both involving the idea of reposing a motion planning problem as a search problem in the Configuration Space. The main focus of this report is to illustrate how a search-based approach, originally proposed to solve a search problem, can be accommodated for the use of planning the motion of a simulated two-link arm. Moreover, the performance analysis of three different search algorithms, Depth First Search (DFS), Breadth First Search (BFS), and A-Star (A*) for a grid search will be provided to show the conclusion that A* is the best search-based algorithm out of the three to use in practice because it generates the most efficient arm movement with a slight tradeoff in a runtime.
Anti-collision Safety Level Evaluation Method Based on Matter Element Extension Model

Safety evaluation can make anti-collision safety management post-processing as pre-prevention, which can maximize the air traffic safety. For this purpose, an evaluation method based on Analytic Network Process (ANP) and matter element extension model is proposed for anti-collision safety level. In the method, firstly, on the basis of Safety Management System (SMS) implemented by International Civil Aviation Organization (ICAO), from the perspective of the safety management construction process, an evaluation index system was established for anti-collision safety level, and then ANP was introduced to determine the weight of the indices in the index system; Finally matter element extension model was constructed to realize the evaluation of anti-collision safety level. The case analysis shows that, the evaluation results obtained by the proposed method are objective and scientific, and can reflect the development trend and change characteristics with strong operability and guidance, providing a reliable realization for anti-collision safety level assessment.
Research on Emotion Recognition Based on Deep Learning Mixed Modalities

With the rapid development of artificial intelligence and machine learning in recent years, emotion recognition has gradually become an important research topic. Emotion recognition in one direction has a good research foundation after long-term development, and from multiple directions, more effective information can be extracted, thereby improving the accuracy of emotion recognition. This paper analyzes from the perspective of emotional recognition of physiological signals such as brainwave signals and facial emotion recognition, respectively, preprocessing, feature extraction, SVM feature classification, LSTM combined with convolutional neural network emotion recognition for the acquired signals. And the accuracy of mixed-modal emotion recognition is compared. Compared with single facial expression emotion recognition, mixed-modal emotion recognition extracts more feature information and has a higher accuracy.
Research and Application of Artificial Intelligence and Robot Technology in Challenge Arena Competition

With the expansion of the application field of intelligent robot, people expect intelligent robot to serve human beings in more fields and complete more complex work instead of human beings. However, the environment of intelligent robot is often unknown and difficult to predict. It is becoming more and more difficult to analyze and design the behavior of intelligent robot manually. Combined with the actual situation, this paper analyzes the application of robot technology in the competition, and designs a new design scheme of Wushu challenge arena robot, which carries out innovative design from the aspects of mechanical structure, perception system, control system, strategy response, motion distribution and so on. The results show that compared with the traditional scheme, the structure of this scheme is reasonable and stable, and the motion and computing performance are more outstanding.
Application of Digital Media Interactive Technology in Remote Control of Picking Robots

In the design process of the picking robot automation system, this paper introduces the picking robot communication and interaction system based on the principle of interactive audio-visual English learning. The purpose is to improve the control efficiency of the picking robot. The system innovation proposes to collect human joint skeleton information based on the depth camera Kinect. In this paper, information such as joint position offset and bone rotation angle are calculated through space vector calculation. Then this paper uses the voice control technology to adjust the working posture of the picking robot to realize the video and audio interaction between the administrator and the picking robot. It can effectively improve the operation quality of the picking robot and reduce the failure rate of the picking robot. The test results show that the picking robot can accurately identify the voice-controlled signal at the remote end. In this paper, the accuracy of the action according to the command signal is high, which can meet the design requirements of high-precision picking robots.
Global Context Enhanced Item Learning for Session-based Recommendation

Session-based recommendation is a challenging task, which aims at recommending items based on user behavior sequences. Robust item representation learning has a great impact on improving recommendation performance. Existing session-based recommendation methods randomly initialize items into low-dimensional vectors, and then simultaneously train the item representations when training the session-based recommendation model. Existing approaches do not make full use of user historical interaction data. In order to make full use of user interaction data, we first construct a global item transition graph based on all user interaction data, and then apply a graph neural network to learn the item representation in the item transition graph. The downstream session-based recommendation models learn user preference and give recommendation results based on the global item representation. Our proposed global context item learning method can effectively utilize the global item transition knowledge, thus can learn rich item representations. We conduct inclusive experiments on three common datasets, and the results show the effectiveness and the efficiency of our proposed framework.
Comparison of Sentiment Analysis Techniques for Twitter posts classification

Social media is the most popular and fast data-generating application on the global network, boosting content research. With this kind of data readily available on the internet, sentiment analysis is a fundamental approach that serves as the foundation for many natural language applications. Sentiment analysis is the best effective technique for determining if an evaluation is positive, negative, or neutral. This article analyses seven alternative models of emotion methods. Execute sentiment analysis in this research with machine learning and deep learning techniques. The sentiment of tweets in all datasets evaluates using all available methodologies. Convolutional Neural Network attained an accuracy of 97.28 percent for 3-class classification on the vaccination dataset, while the second-highest accuracy on the same dataset was 96.85 percent. This research seeks to compare the optimal accuracy of a machine learning or deep learning model based on different dataset extraction methods. The dataset studies various issues and differentiates the file size or the number of tweets in each dataset from the smallest to the largest size. When we evaluated different algorithms in this study, our goal was to see how the number of tweets affected the accuracy of the algorithms, and we obtained a dataset about Blockchain from Twitter.
The Yellow River Walker Beach Garbage Robot

This paper introduces a garbage cleaning robot based on the development technology of Raspberry Pi in the Yellow River beach. This paper introduces a kind of beach garbage cleaning robot by introducing the problem of garbage in the Yellow River beach in reality. This paper also introduces the related technologies of robots, and proposes and designs a robot solution for beach garbage cleaning.
Arabic Code Generation based on Four Direction of Human Eye

In this paper, we built a Desktop Application for a new encoding method presented to help disabled Arabic-speaking people. The proposed encoding method depends on the direction of the pupil of the eye so that the most frequently used letters have the shortest codes. The language characters are converted into a sign of the direction of movement of the human eye's pupil. The proposed system is a text entry system consisting of three parts. The first part included a CNN-based algorithm for evaluating the direction of the eye pupil, and the second part was an algorithm for building an encoding triple tree to generate the symbol for Arabic letters, while the third part involved Translate codes to its corresponding letter. The system was tested by a number of well-trained participants. The accuracy of the system goes up to 99.3% while the system's sensitivity reached 88.5% and total specificity 99.6%. also SCR and MSD metrics are computed.
Face Skin Color Detection Method Based on YUV-KL Transform

In recent years, with the development of activities such as e-commerce, face recognition has become a possible way to verify identity characteristics. The purpose of this paper is to study the method of face skin color detection based on YUV-KL transform. A method based on skin color and facial features is proposed to detect human faces. First, the skin color information is used for detection, and then the face features are used for verification. The first is face detection based on skin color. The work to be done in this process includes: the selection of color space, the analysis of YCbCr color space, the introduction of Gaussian density function estimation skin color model, and the use of simple skin color model. The second is KL transform to extract face features (features of eyes and mouth), and verify the detected faces. YUV-KL color space and KL feature extraction are used in face detection, two important contents, and compared with the traditional face skin color detection method. The experimental results show that the number of missed detections of this method is only three images.
Research on the Construction System of Computer Big Data Security Platform Based on Artificial Intelligence

This paper analyzes the current network security situation in the era of big data. At the same time, the author summarizes the theory of network security management from the aspects of risk control and security assessment. On this theoretical basis, a network security management platform based on artificial intelligence big data is constructed. The platform has built a complete network security situational awareness and early warning architecture that integrates data collection and sorting, data storage, offline analysis and discovery, real-time correlation detection, threat warning and situation presentation, and supports the whole process of security event processing. The experimental results prove that the multi-model ensemble algorithm used in the proposed system combines the advantages of each model, making the overall accuracy rate up to 99.24%.
Combination of Stream and Block Image Encryption Algorithms in Pixel Scrambling Using Henon Map

In recent years, sending digital photos over open networks has become a serious privacy concern. Sadly, there are a lot of privacy and security risks present despite the environment's suitability and great benefits. The novel image encryption technique proposed in this paper is based on the two-dimensional Hénon map, which is used twice to produce the random sequences required for confusion and diffusion. The 2-D Hénon map initial random sequence key is used to stream-scramble the pixels in each and every block of the image during the confusion stage. At the diffusion stage, the second random sequence key is used in a triple XOR structure to change the pixel positions and create an encrypted image. The experiments' findings show that the suggested approach fixes the problems with traditional encryption methods. The proposed method costs less to compute and performs better on many security tests. Because of its size, the key space cannot be protected from brute-force attacks. The histogram of the encrypted image differs from the original image and is evenly spaced. The results of the correlation test reveal no correlation between the adjacent pixels. The suggested algorithm is sensitive to the key value, and even small changes result in a different image. Therefore, real-time image encryption applications over open networks are appropriate for the novel approach.
Whale Image Retrieval with Fused Mask Information

Content-based image retrieval (CBIR) aims to search images from the gallery that are most similar to a query. In this work, we propose a novel image retrieval framework called TM-Net (Two-branch network with Mask). The core of this approach is to incorporate information inherent to the image in the input phase of the network, which operates to force the network to focus more on the object itself in the image and to extract a feature descriptor that is strongly correlated with the object. To further the feature extraction, our network adopts a two-branch structure, where one branch extracts global features and the other branch extracts local features. In addition, we use a classification method with metric learning, so that the loss function of metric learning and the classification loss complement each other to increase the inter-class distance and decrease the intra-class distance. Moreover, to better sample the training samples, we use a hard sample sampling strategy. we conduct extensive experiments on the whale dataset, and the experimental results show that the method is simple and effective, and surpasses the current state-of-the-art methods. We obtained top-1 score of 94.6% on the whale test set, which is better than existing methods.