Reviewerly/exHarmony huggingface dataset:
Graphic and Text Emotional Analysis based on Deep Fusion Network

Nowadays, multimodal sentiment analysis has become a popular field, which combines traditional text, pictures, videos and other different modalities to perform sentiment analysis. Multi-modal sentiment analysis usually first obtains the emotional features of each modality, and then fuses the emotional features of different modalities. Among them, single-modal feature extraction is very important, and multi-modal information fusion is also very important. Then, our document applies a multiplicate feeling analytical model for image and word based on deep learning network. Our model is primarily separate into word feature fetch former, draft feature fetch shape, feature melt former, and we made feeling analysis in the end. In text feature extraction, we use an improved CNN model that removes the pooling layer to increase the attention mechanism; Image Feature Extraction We use a modified dense block method for image recognition feature extraction, and finally we use an attention mechanism to fuse modalities and perform sentiment analysis. Last, Comparison results with a lot of experiments that the IDFN model proposed in this paper is significantly improved compared to the traditional sentiment analysis method.
Correlation power analysis attack on software implementation of TRIVIUM stream cipher

Power analysis attacks are a category of attacks against cryptographic implementations. In this case, the power consumption of a cryptosystem is analysed to extract its secret values such as secret keys and key streams. This has become a huge threat to modern day cryptosystems. Therefore, identifying cryptographic implementations which are vulnerable to power analysis attacks is very important. Many studies have been carried out on power analysis attacks on block cipher implementations, but relatively less number of studies have been carried out on power analysis attacks on stream cipher implementations. This paper presents a power analysis attack on a software implementation of TRIVIUM stream cipher. In order to analyse the power consumption, correlation power analysis (CPA) is done, and the keystream is successfully recovered.
Unsupervised Labor Intelligence Systems: A Detection Approach and Its Evaluation

In recent years, job advertisements through the web or social media represent an easy way to spread this information. However, social media are often a dangerous showcase of possibly labor exploitation advertisements. This paper aims to determine the potential indicators of labor exploitation for unskilled jobs offered in the Netherlands. Specifically, we exploited topic modeling to extract and handle information from textual data about job advertisements for analyzing deceptive and characterizing features. Finally, we use these features to investigate whether automated machine learning methods can predict the risk of labor exploitation by looking at salary discrepancies. The results suggest that features need to be carefully monitored, e.g., hours. Finally, our results showed encouraging results, i.e., F1-Score 61%, thus meaning that Data Science methods and Artificial Intelligence approaches can be used to detect labor exploitation—starting from job advertisements—based on the discrepancy of delta salary, possibly representing a revolutionary step.
An Attendance System Using the Face Extraction and Recognition Technique Using PCA Algorithm

Face detection is the most critical and first step in the attendance management system. The human face is non-rigid and has many differences in visual situations, scale, clarity, poses, and rotation. The precise and reliable identification was a challenge for the researcher. A variety of methods and techniques are suggested, but due to many variations, no one technique is very effective for all sorts of faces and pictures. Many techniques show good results under certain conditions, and others are successful for various types of images. Image-discriminating methods are commonly used for pattern and image analysis. Specific forms of prejudice are discussed.
Human Emotion Recognition System using Residual Connections

The goal is to use a Multi-Branch Cross-Connected Convolutional neural network to construct a new model for expression detection based on a novel face decomposition. CNN, residual connections, and Multi Networks are all included in this strategy. Other approaches may not be able to extract as many features as this method. This method adds a shortcut link, resulting in a highly smooth data or feature flow, as well as good feature extraction. Some methods are not that great in extracting complete features; this will be sorted out in this method. This method can recognize all the emotions, the emotion which shows the highest value is our output. The data flow in Single-structure CNN is not smooth, and feature extraction is insufficient. The proposed method can perform well in terms of emotion recognition and has a high level of accuracy. More fluid data transfer between networks improves feature extraction and increases emotion recognition performance.
Heat-Map Algorithm Based Multi-robots Path Planning Method

Robot conflicts elimination and efficiency improvement from a global perspective are important issues in intelligent storage systems. In order to improve the operation efficiency of intelligent storage system more effectively, a heat-map Algorithm by combing the reservation table in this paper. Firstly, a small storage grid model applicable to multiple storage modes is established. Secondly, considering the frontal collision problem of multiple robots, an improved reservation table is established, which greatly reduces the storage space occupied and improves the operation efficiency at the same time. In addition, the heat map algorithm is added to reasonably allocate the tasks, avoid the congested area and realize the dynamic assignment of tasks. Finally, the effectiveness of the proposed design scheme is demonstrated by simulation.
Boson Sampling and Linear Optical Circuits

Boson sampling is a non-universal quantum computation model where single photons in a linear optical circuit naturally perform a computational task that is believed to be difficult to solve classically. In this review, we introduce the foundation of the boson sampling along with a simple example. Further, we present our universal linear optical circuit as an example of the required device for boson sampling.
Features of hydrogeochemistry of natural waters the tracts of the Kastropol (southern coast of the Crimean peninsula)

This paper presents comprehensive hydrogeochemical studies of the natural waters the tracts of the Kastropol. The studied waters are mainly characterized by HCO Ca and HCO Mg-Ca composition with total mineralization, which varies from 220 to 2306 mg/dm and silicon content from 0,78 to 6,08. The geochemical parameters of the environment vary from the reductive to oxidative setting with Eh from -146,6 to +275,9 mV; pH from 7,4 to 8,5, and O from 1,80 to 7,73 mg/dm. Natural waters were divided into two geochemical groups by geochemical coefficients that differ from each other in their relations Ca/Na, Ca/Mg and Si/Na. B, Br, I, Re и Au accumulate in the waters. Li, B, Sr, Re and Au have a very strong migration ability in solution. According to the indices of water pollution with heavy metals, a high degree of enrichment of the waters of wells in the Partizanskoye village was established.
VSA: Adaptive Visual and Semantic Guided Attention on Few-Shot Learning

Training models with only a few samples often bring overfitting and generalization problems. Moreover, it has always been challenging to identify new classes based on small samples. However, studies have shown that humans can use prior knowledge such as vision and semantics to learn new categories from a small number of samples. We propose a bimodal attention mechanism (VSA) based on vision and semantics to better use this prior knowledge like humans. VSA can adaptively combine information from both visual and semantic modalities to guide visual feature extraction, that is, which features should be paid more attention to during feature extraction. Therefore, the new category is more discriminative even if only one sample exists. Meanwhile, our extensive experiments on miniImageNet, CIFAR-FS, and CUB demonstrate that our bimodal attention mechanism is effective and achieves state-of-the-art results on the CUB dataset.
Frequency Hopping Signal Recognition Based on Horizontal Spatial Attention

Frequency hopping (FH) technology is one of the most effective technologies in the field of radio countermeasures, meanwhile, the recognition of FH signal has become a research hotspot. FH signal is a typical non-stationary signal whose frequency varies nonlinearly with time and the time-frequency analysis technique provides a very effective method for processing this kind of signal. With the renaissance of deep learning, methods based on time-frequency analysis and deep learning are widely studied. Although these methods have achieved good results, the recognition accuracy still needs to be improved. Through the observation of the datasets, we found that there are still difficult samples that are difficult to identify. Through further analysis, we propose a horizontal spatial attention (HSA) block, which can generate spatial weight vector according to the signal distribution, and then readjust the feature map. The HSA block is a plug-and-play module that can be integrated into common convolutional neural network (CNN) to further improve their performance and these networks with HSA block are collectively called HANets. The HSA block also has the advantages of high recognition accuracy (especially under low SNRs), easy to implant, and almost no influence on the number of parameters. We verified our method on two datasets and a series of comparative experiments show that the proposed method achieves good results on FH datasets.
Monocular 3D Face Reconstruction with Joint 2D and 3D Constraints

3D face reconstruction from a single image is a challenging problem, especially under partial occlusions and extreme poses. This is because the uncertainty of the estimated 2D landmarks will affect the quality of face reconstruction. In this paper, we propose a novel joint 2D and 3D optimization method to adaptively reconstruct 3D face shapes from a single image, which combines the depths of 3D landmarks to solve the uncertain detections of invisible landmarks. The strategy of our method involves two aspects: a coarse-to-fine pose estimation using both 2D and 3D landmarks, and an adaptive 2D and 3D re-weighting based on the refined pose parameters to recover accurate 3D faces. Experimental results on multiple datasets demonstrate that our method can generate high-quality reconstruction from a single color image and is robust for self-occlusions and large poses.
Arbitrary Scale Super-Resolution Neural Network Based on Residual Channel-Spatial Attention

In recent years, the performance of convolutional neural networks in single-image super-resolution has improved significantly. However, most state-of-the-art models address the super-resolution problem for specific scale factors. In this paper, we propose a convolutional neural network for arbitrary scale super-resolution. Specifically, given a range of scale factors, the proposed model can generate super-resolution images with non-integer scale factors within the range. The proposed model incorporates a channel-spatial attention block in which the scale factor is also provided. This module recovers the most relevant information from the low-resolution image given the scale factor and enhances the upsampled image before generating the high-resolution target image. This channel attention block allows learning the channel and spatial dependencies. Additionally, we incorporate global residual learning so that the model recovers the details of an upsampled low-resolution image by interpolation. We evaluated the proposed method through extensive experiments on widely used benchmark datasets for single-image super-resolution. In order to assess the performance of the model, we used the peak signal-to-noise ratio and the structural similarity index measure. The proposed model achieves an average of 35.36, 31.78, 29.62 for peak signal-to-noise ratio, and 0.9410, 0.8828, 0.8334 for structural similarity index measure for the standard evaluation scale factors ×2, ×3, ×4, respectively. The experimental results show a better performance of the proposed model over other state-of-the-art models for arbitrary scale super-resolution, and are competitive with models trained for specific scale factors.
Tea Picking Path Planning Based on Ant Colony Algorithm

In order to improve the efficiency of tea picking, a scheme combining ant colony algorithm and tea picking technology is proposed to plan the picking path. In this scheme, k-means clustering algorithm is used to divide the tea picking regions with irregular growth. The ant colony algorithm is used to carry out path planning for tea in different picking areas, and the best picking effect is selected according to the simulation results. To solve the problem of long searching time of ant colony algorithm, the condition of iteration termination is changed to adaptive value, and the optimal route after the last iteration is recorded as the initial value of the next iteration. The simulation results show that the best effect is to divide two picking regions. The improved ant colony algorithm reduces the operation time and the planned picking path is shorter, which can enhance the real-time tea picking and improve the efficiency of tea picking.
Penerapan Model Probing Prompting Untuk Meningkatkan Keterampilan Berkomunikasi Siswa

&#x0D; &#x0D; &#x0D; &#x0D; This study aims to: 1) determine students' communication skills before applying the Probing Prompting Learning Model in Civics lessons for fifth grade students, 2) determine students' communication skills after applying the Probing Prompting Learning Model in Civics lessons. fifth grade students, 3) improve students' communication skills in Civics material after applying the Probing Prompting learning method for fifth grade students. This study uses a Classroom Action Research approach. The data collection techniques in this study are observation and tests. The results showed that: 1) Through the Probing Prompting learning model there was an increase in the communication skills of fifth grade students at SD Negeri 101390 Batang Pane 1 in Civics learning. 2) Students' communication skills were obtained through the application of the Probing Prompting learning model, namely the average score of students' communication skills in the first cycle was 76.88 and increased by 94.75 in the second cycle. The Probing Prompting learning model has been implemented according to the learning scenario in class V at SD Negeri 101390 Batang Pane 1.&#x0D; &#x0D; &#x0D; &#x0D;
Component Based Pre-filtering of Noisy Data for Improved Tsetlin Machine Modelling

Tabular data with few observations and many features are common in the healthcare domain. With its rule-based approach to data modelling, the Tsetlin Machine has considerable potential to be a valuable tool in healthcare data analysis by providing interpretability to medical personnel. However, the performance of Tsetlin Machine models may be hampered by the presence of noise, which may often be the case with healthcare data due to individual differences across patients. This study shows that intelligent pre-filtering of healthcare measurement data using so-called component-based methods, such as Principal Component Analysis or Partial Least Squares Regression, can be beneficial for the performance of Tsetlin Machines. Modelling four healthcare data sets shows that the Tsetlin Machine achieved better predictive performance on pre-filtered data for data sets with high features-to-observations ratios.
Research on heavy truck recognition algorithm based on deep learning

The Chinese economy has developed rapidly by taking advantage of convenient and rapid freight transport. Heavy trucks have always attracted much attention as the leading force in freight transportation. Although heavy trucks have significant advantages in freight transportation, their high emissions that cause air pollution and high accident rates have always been criticised. Monitoring of heavy trucks has been improving in China, and this paper adopts deep learning to identify heavy trucks to strengthen their supervision. Given the multi-target recognition problem in the actual scene, this paper uses a one-stage algorithm for target detection. The representative network SSD (Single Shot Multi-Box Detector) and YOLO (You Only Live Once) are compared. YOLO adopts the YOLOv5s structure, and the SSD network is subdivided into two networks by replacing the backbone structure. One backbone structure is VGG, and the other is Mobilenetv2. The final experimental results show that the SSD network with VGG as the backbone structure achieves the best Map value of 93.24%, which is 6.02% and 8.14% higher than the SSD and YOLOv5s training models with Mobilenetv2 backbone structure, respectively.
A Heterogeneous Database Encryption Algorithm Based on B/S Structure

In order to solve the problem that the key of the traditional method is uneven between the user and the authority center, which leads to the long encryption time of a heterogeneous database, a heterogeneous database encryption algorithm based on the B/S structure is proposed.By comparing the advantages and disadvantages of the C/S structure and B/S structure, the framework of heterogeneous database encryption based on the B/S structure is constructed.Based on the encryption architecture framework of the B/S structure, the key management mechanism is given, and the key of the authority management center at all levels in the server user information management system is generated, as well as the user's key and the user's master key in the corresponding authority center.Combined with the access policy tree encryption to calculate the plaintext data on the server-side of the heterogeneous database, the data that need to be protected is converted into ciphertext form, and the heterogeneous database encryption is completed.The simulation results show that the proposed algorithm can effectively complete the heterogeneous database encryption and protect the database security, and the response time to each service of the database server is shorter than the encryption algorithm compared with the experiment.
Joint training with the edge detection network for salient object detection

The U-shaped network has great advantages in object detection tasks. However, most of the previous salient object detection studies still suffered from inaccurate predictions affected by unclear object boundaries. Considering the complementarity of the information between salient object and salient edge, we designed a new kind of network to effectively perform the joint training with edge detection tasks in three steps. Firstly, we added a prediction branch on the bottom-up pathway for capturing the edge of salient objects. Secondly, salient object features, global context, integrated low-level details, and high-level semantic information are extracted by the method of progressive fusion. Finally, the feature of the salient edge is concatenated with that of the salient object on the last layer in the top-down pathway. Since the salient edge feature contains much information about edge and location, the feature fusion can locate salient objects more accurately. The results of experiments on five benchmark datasets demonstrate that the proposed approach achieves competitive performance.
Content Is King, Form Is Gold —On the Reform of Online Teaching Content and Innovation of Teaching Mode

Online teaching as an effective means to deal with the epidemic large-scale teaching, because of its remote, flexible, real-time, convenient characteristics, quickly accepted by teachers and students, social recognition, the realization of “ceasing teaching, ceasing school”. In the fight against the epidemic, famous online teachers and universities gathered together, and golden courses and MOOCs gathered together, thus creating an “education revolution”. How to grasp the relationship between content and form? How to optimize teaching content? How do teaching activities unfold? How to use the teaching platform is a thorny problem for online education. Starting from the relationship between form and god, this paper advocates the combination of form and spirit, the unity of form and spirit, the double work of form and spirit, and the perfection of form and spirit, and puts forward the viewpoint of “content is king, form is gold”. “Content is king” focuses on teaching content, teaching elements and teaching objectives, while “form is gold” highlights teaching methods, teaching modes and teaching means. The combination of the two can form the “best partner”., focusing on the “shape” and “god” fusion around teaching concept, teaching orientation, teaching strategy, teaching design, teaching means, teaching platform, teaching preparation, teaching process and teaching evaluation, teaching resources, teaching quality project and puts forward the content reform feasible measures and model innovation, promote to realize the unity of teaching and learning, knowledge and the transformation.
From &amp;quot;Law + Engineering&amp;quot; to Engineering Law: Current Situation, Goals and Value Implications

The study of legal issues in the productivity development of engineering activities at its core is a response to the orderly needs and risk regulations of complex engineering fields. It is a realistic need for the legal review of engineering activities and knowledge. Since the concept of engineering law was put forward, it has faced the inherent disadvantage of insufficient endogenous theory. It is difficult to realize research on engineering legal relationships with goal orientation, value orientation, and integrity in the horizon of technology first or law first. The contradiction between the surge in engineering activities and large number of engineering legal issues arising therefrom and the inadequacy of basic theoretical research in engineering law, has become a bottleneck that restricts the orderly development of the field. Therefore, it is crucial to conduct research on the status quo of engineering law, target its location, explore its value, and promote "law + engineering" to achieve engineering law on the basis of discipline integration.
TUR: Utilizing Temporal Information to Make Unexpected E-Commerce Recommendations

Unexpectedness recommendations are getting more attention as a solution to the over-specialization of traditional accuracy-oriented recommender systems. However, most of the existing works make limited use of available interaction information to compute distance and neglect the fact that varying time intervals for recommendations would lead to different perceptions of unexpectedness from users. In this work, we propose a novel Temporal Unexpected Recommendation (TUR) approach to improve e-commerce recommendations’ unexpectedness. Specifically, we consider the complementarity of both implicit and explicit distances, modeling unexpectedness from the latent space (i.e., embedding vectors) and the side information (i.e., item taxonomy) respectively. Meanwhile, we import a module based on the time-aware GRU to leverage the impact of timeliness on recommendation unexpectedness. Experiments on a large-scale e-commerce dataset containing real users’ feedback show that TUR significantly outperforms the baselines in enhancing unexpectedness while maintaining a comparable accuracy level.