Reviewerly/exHarmony huggingface dataset:
Profil Kecerdasan Emosi Siswa di SMK Negeri I Bonjol

This research was conducted based on the phenomenon stated at SMKN I Bonjol there were several students who were less able to control their emotions with indications: anxiety in facing exams, students who were unable to control fear when studying with teachers they didn't like or were angry, and uttered harsh words to his friends, students also like to be alone when they have problems, and students also like to scribble on their desks or notebooks, students also like to use harsh words and high tones. The purpose of this study was to find out how big the level of Emotional Intelligence Profile of Students at SMKN I Bonjol. The research method uses a quantitative descriptive approach. The population in this study were 252 class XI students. The research sample consisted of 38 students who were taken using a random sampling technique. Data was collected using the questionnaire method with the Liker Scale instrument and then processed using data analysis techniques using statistical tests with the help of SPSS 16.0. the technique used in processing this research data is statistical test. In this study to see how big the level of Student Intelligence. is to perform statistical tests. The results of statistical tests that have been carried out through the SPSS application, namely that it can be seen that the overall average value is 3.40 with a percentage of 65.59% and a standard deviation of 157.1, it can be concluded that the profile level of the emotional intelligence of students at SMKN I Bonjol is at a high level.
A research on the fruit recognition software design based on augmented reality technology

In order to solve the problem of "poor effect of spreading fruit knowledge through paper books, pictures and videos", this paper adopts augmented reality technology to recognize fruit and spread relevant knowledge. The specific operation process is as follows: camera shooting, Internet uploading of fruit pictures, server recognition of the relevant fruit, the relevant data of the fruit will be transferred to the user's display device, superimposed on the real fruit. With the help of the augmented reality software designed in this paper, fruit knowledge can be displayed in three dimensions, enhance the interaction and immersion of learning, improve learners' interest, and expand their life knowledge.
Joint training based on adversarial extraction of auxiliary training dataset

Task-driven Language Modeling (TLM) can reduce the amount of calculations by an order of magnitude in NLP tasks but achieve the effect of matching the BERT-like large model. It uses BM25 to retrieve relevant data subset from general datasets and combine them with task dataset for joint training, but the extraction process does not consider semantic information, and IRGAN can further extracts semantic information features in the way of adversarial training, and the combination of them can use semantic information to enhance the relevance between the auxiliary training dataset and the labeled task dataset, and then through linearly decreasing joint training in for low-resource tasks have been improved compared to TLM.
Focal ViT: image transformer catches up with CNN on small datasets

Recent advances of transformers have brought new trust to computer vision tasks. However, on small dataset, transformers is hard to train and have lower performance compared to convolutional neural networks. We make vision transformers as data efficient as convolutional neural networks by introducing focal attention. Inspired by the local attention networks, we constrained the self-attention of ViT to have multi-scale localized receptive field. We provide empirical evidence that proper constrain of receptive field reduce the amount of training data for vision transformers. Our best model reaches 83.16% accuracy when training from scratch on CIFAR-100 which is a significant improvement in data efficiency over the previous transformer. We also perform analysis on ImageNet to show our method does not lose accuracy on large datasets.
A mobileBERT-based preset class fusion text classification algorithm

Grassroots governance staff are submitting increasing amounts of text data to governance system due to encouragement of intelligent governance work. Misclassified work order data results in data loss, leading to wasted labor and material resources. Hence, effective text classification techniques are needed for correction. Training results are insufficient to suit industry needs due to limited work order data and difficult data conditions. For difficulties above, this research presents a mobileBERT-based preset class fusion text categorization algorithm (Preset class fusion BERT, PCFBERT). Templated preprocessing of address information, label representations produced by algorithm encoder are separated from input data representations to compute contrastive loss function and improve model performance on one hand and used as classifier input to produce final prediction results on the other. Traditional text classification algorithms cannot guarantee high speed and accuracy in face of tiny samples of actual data and complex data conditions. Suggested technique solves this problem. Templated preprocessing of address names reduces noise and makes dataset information more reliable. Label fusion and contrast learning techniques based on mobileBERT improve model data representation in this paper. Triple mapping module fuses pre-defined label class information into classifier and better utilize global information to improve decision-making. Combining strategies above improves structure robustness and performance without increasing model size. This research evaluates recommended strategy using simulated data from desensitized work orders and compares it to many popular text classification methods. Suggested method outperforms popular text classification algorithms in classifying work order text data. Same higher performance is achieved in two open Chinese multiclassification text datasets.
Design and development of a remotely operated vehicle and its control system

This paper describes a design for a new type of Remotely Operated Vehicle (ROV) and a system to control it. The ROV and its control system are part and parcel. The control part of the ROV includes the Data Acquisition Program. The Data Acquisition Program is a GUI-based computer program that allows the operator of the ROV to visualize telemetry data from the ROV. It provides near-instantaneous communication with the ROV. The computer-aided design of the prototype is made using Autodesk’s Fusion 360. Then the design was converted to Stereolithography (STL) format and passed through a slicing software called Cura in preparation for 3D printing. An Ender 3D printer was used to manufacture the parts of the design, using Polylactic Acid Thermoplastic Polymer. Computational Fluid Dynamics is conducted to analyze the fluid hydrodynamics (water and pressure flow rates) around the hull of the remotely operated vehicle and the drag force acting on it. Several experimental results have shown the effectiveness of the remotely operated vehicle design.
Temporal action proposal generation with self-supervised pre-training transformer

Temporal Action Proposal Generation (TAPG) is a challenging task at present, which aims to accurately generate temporal proposals that are likely to contain human actions in an untrimmed video. Inspired by the successful pre-training strategy applied by the transformer-based image object detection method, we propose a Self-Supervised Pre-Training transformer (SSPT) method for the TAPG task to train an action locator without any labels. As far as we know, this is the first work to explore self-supervised pre-training for transformer for TAPG. Specifically, the pre-training strategy leverages the transformer model to model the contextual information and we leverage transformer to capture contextual semantic information and global temporal dependencies of actions in the entire video can better accomplish the TAPG task. And in the pre-training process, we adapt Random Query Segments, which randomly crop multiple segments from the original video. We treat the segments as pseudo-labels and input them as queries to the transformer decoder. The pretext we designed is to locate the start time and end time of the pseudo-labels in the original video. Extensive experiments on THUMOS14 demonstrate the effectiveness of SSPT, the results show that it improves the performance of baseline and leads to a higher recall for the Temporal Action Proposal Generation task.
Siamese network tracker with attention mechanism

The object tracking algorithm based on the deep Siamese network has a low tracking success rate and low robustness under the conditions of target illumination changes, occlusion and deformation. Therefore, this paper proposes a deep Siamese network tracking algorithm with attention mechanism based on SiamRPN++. First, an attention mechanism is added to each layer of the feature extraction network ResNet50 to calculate the importance of each channel, so that the model can obtain more useful information. Second, since the shallow features focus on the details of the target, the deep features focus on the semantic information of the target. Therefore, a feature fusion method based on the attention mechanism is proposed to fuse the deep and shallow features to enhance the expressive ability of the features. On the OTB100 and LaSOT datasets, the success rate of our tracker is 70.1%, 51.6%. Compared with SiamRPN++, it has increased by 1.1% and 2.4%.
Optimization and simulation prediction of war strategies based on Markov networks and BP neural networks

Both sides in contemporary warfare must thoroughly consider their strategies and implement effective tactics to increase their offensive or defensive capabilities. It is critical to emphasize the strengths and weaknesses of one’s own side to establish a balanced and stable situation on the battlefield. This paper presented a suggested method that employed Markov networks to define the weight of nodes in a map, resulting in an optimal predetermined layout. Furthermore, we proposed adopting BP neural network algorithms to give the army autonomy in decision-making. That enabled each unit to develop its own decision-making process and enhance the overall war strategy via repeated simulations.
Predicting effective arguments based on hybrid language model

With the rapid development of information equipment, people's demand for teaching aids is increasing. Among them, the construction of writing aid evaluation system is a challenging task, its difficulty lies in the diversity of writing forms and the diversity of context. In this paper, we propose a Hybrid Model based on an ensemble module, which has the characteristics and advantages of both Roberta and Deberta. Our model includes enhanced mask decoder and disentangled attention transformer layers. At the same time, we used FGM adversarial training to improve model performance. We carried out tests on the Predicting Effective Arguments data set, and experiments showed that the performance of our model far exceeded that of other single models, such as Bert model, Roberta model and Deberta model.
Pipeline valve operation action recognition method based on YOLOv5

Although extensive research has been carried out on Human Action Recognition (HAR) and HAR has been applied in many fields of computer vision, little studies focus on the operation detection of workers in petrochemical plants. There are thousands of inflammable chemicals and large amount of toxic gas during the production in petrochemical plants, which would cause serious disaster. Thus, we argue that real-time detection of labors’ actions is required to keep their safety and the production efficiency of petrochemical plants. But most of state-of-the-art models of HAR need a lot of training time and most of them have huge model size which makes it is hard to deploy them on edge devices. To solve this problem, this paper proposes an architecture to detect labor’s operation of pipeline valves in petrochemical plant based on YOLOv5. We calculated relative positions of different objects which were detected by YOLOv5 and added an estimation layer behind output layer of YOLOv5, so that we could use our architecture to estimate labors’ actions. Finally, our experiments show that our architecture can estimate the worker’s actions with 97.5% precision. The precision, the recall, and the mAP@0.5 of object detection is 98%, the mAP@0.5:0.95 is 85%.
Expand prompt verbalizer by extracting knowledge for Chinese text classification

Prompt-based Pre-trained Language Models (PLMs) have demonstrated their superior performance on a wide variety of downstream tasks. In particular, the performance of prompt tuning has significantly outperformed traditional fine-tuning in the zero-shot learning and few-shot learning scenarios. The core idea of prompt-tuning is to convert different downstream tasks to mask language modeling problems through prompts, which can bridge the gap between pre-training tasks and downstream tasks for better results. Verbalizer, as an important part of prompt-tuning, can largely determine the final performance of the model, but the design of the Chinese-based verbalizer is yet to be fully explored. In this paper, we propose a method to expand the verbalizer by extracting knowledge from the training set based on a Chinese text classification task. In brief, we first segment the Chinese training set, then filter the words that can express the semantics of the labels by semantic similarity, and finally add them to the verbalizer. Extensive experimental results on multiple text classification datasets show that our approach significantly outperforms ordinary prompt-tuning and outperforms other methods for constructing the verbalizer.
Epidemic prediction based on entropy-improved factor analysis and WOA-optimized BP network algorithm

The new Coronavirus epidemic has had a huge impact on the economy, politics, and culture worldwide. However, it is very difficult to obtain accurate data on the new crown epidemic due to various uncertainties, such as the difficulty of detection. In this paper, we use objective and real Baidu search indexes as the basic data set and use factor analysis with the improvement of entropy method to reduce the dimensionality of Baidu search index data to solve the problem of fixed parameters caused by its excessive dimensionality. After that, the WOA algorithm is used to optimize the parameters of the conventional BP neural network, thus making the fit and accuracy greatly improved, which is of great practical significance for the prediction of epidemic data.
Hand gesture recognition model based on surface EMG

Four channels of surface EMG signals were collected from six gesture movements, and their time-domain and time-frequency domain features were extracted. Principal component analysis is used to reduce the dimension of features. For the dimension-reduced features, the decision tree is used to build a gesture recognition model. The experimental results show that the overall accuracy of the model for classifying six kinds of actions is more than 97%, which has good application value.
Overcome STSF phenomenon in catastrophic forgetting

Catastrophic forgetting is an undesirable phenomenon in convolution neural networks and iCarl is an effective algorithm for preventing catastrophic forgetting. However, a potential defect that more similar tasks result in severer catastrophic forgetting (STSF) in iCarl is explored in this work. The reason of STSF is that similar tasks are prone to occupy similar feature channels and similar feature representations, thus they can be replaced by each other easily. Based on these findings, a novel method Similar Margin Loss (SML) is proposed. SML aims to make feature representations of samples from the same task compact while making feature representations from the different tasks differentiable in the feature space. Experiment results show that SML is effective in alleviating STSF.
Speech recognition of speaker identity based on convolutional neural networks

Recent advances in the field of speaker recognition (SR) have shown very accurate and high-performance algorithms. When the amount of data is small, its performance drops dramatically [1]. Today, when testing and training involve only small amounts of speech data, identifying the speaker remains a key consideration, as real-life applications can only access speech data for a limited period. Speech recognition-based security systems are one of the main areas of research. In this paper, we will use a graphical CNN algorithm for speaker recognition and compare the effect of different times of speech on the accuracy of the model. The results show that better results can be obtained by using the speech feature data with a deep learning CNN model, with an average accuracy of 86.3%. In addition, the accuracy of speech recognition can be better improved by comparing multiple sets of speech segments with smaller results in this study.
An improved algorithm for object detection based on deep learning

The goal of object detection is to recognize the position and category of all objects in an image, allowing for machine vision understanding. Many approaches have been developed to solve this problem, primarily based on convolutional image feature extraction and deep learning algorithms. However, the current algorithms lack robustness and often have high error rates and a reliance on numerous parameters, making them difficult to apply in practical settings. To address these limitations, this paper presents an improved object detection method based on the R-CNN model. This method leverages the target feature matching module in the R-CNN model to accurately extract features through similarity calculation. Analysis showed that this improved method outperforms traditional models in terms of detection efficiency, especially in complex environments. The proposed method serves as a valuable reference for further research in related fields.
Research on evaluation of ordnance equipment maintenance support capability based on entropy weight and ideal point method

The evaluation of maintenance support capability of ordnance support units involves many factors, some of which are easy to quantify, while others are difficult to quantify, which often brings many difficulties to the evaluators. Therefore, on the basis of establishing the evaluation index system for the maintenance support capability of ordnance equipment, the method of combining entropy weight method and ideal point method is adopted to conduct evaluation research, so as to reduce the subjective randomness of the evaluators and make the evaluation work more scientific and reasonable.
Edge temporal anti-aliasing

Temporal Anti-Aliasing (TAA) is a popular method for eliminating temporal aliasing problems. However, the images simply processed by TAA become blurred and lose some details. In this paper, an improved TAA algorithm named Edge Temporal Anti-Aliasing (ETAA), is proposed. A time iterative edge detection method is designed to enhance the detection accuracy of pixels with temporal aliasing and spatial aliasing. These pixels are blurred and blended, and other pixels are replaced with the pixels of the current frame. Furthermore, an approximate minimum filter is used to eliminate the flickering phenomenon of high-energy noise. Compared to TAA, ETAA outperforms TAA in terms of image detail preservation when the rendering camera moves. Meanwhile, the time cost of proposed method also can satisfy the requirement of real-time rendering. Experiment results show that ETAA can effectively and quickly eliminate the time aliasing when the camera moves, and achieves a better trade-off in flicker, ghosting and blurring.
Object tracking algorithm of Siamese network based on feature fusion and attention mechanism

With the development of computer vision technology, object tracking is widely used in various fields of social life. The traditional object tracking method mainly characterizes the object by considering the designed features, and then realizes the object tracking. With the proposal of the deep learning algorithm, the object tracking field has ushered in a new development. The object tracking algorithm based on the Siamese neural network calculates the target position of the current frame by calculating the response between the template image and the feature map of the image to be searched. It is concise and efficient, but it is prone to tracking drift when facing complex backgrounds. The visual attention mechanism is beneficial to highlight the background and suppress the background. Therefore, this paper introduces an attention mechanism based on the Siamese network to improve the performance of object tracking. In addition, a key point for object tracking based on Siamese network is object and background classification. Compared with high-level features, low-level features have stronger discriminative ability. Therefore, a feature fusion mechanism is also introduced and combined with the attention mechanism to optimize the object tracking algorithm based on Siamese network. Compared with the OTB2013 benchmark evaluation, the algorithm in this paper has better tracking performance than the original algorithm.
An improved image mosaic algorithm for UAV

Aiming at the problems of ghosting and distortion in the process of UAV low-altitude aerial image Mosaic, an improved algorithm for UAV low-altitude aerial image Mosaic was proposed. The algorithm firstly through the SIFT features of UAV image image mosaicing, and then based on the SIFT algorithm in unmanned aerial vehicle (UAV) image matching problems that exist in the process, to improve the image matching in the process of feature extraction methods, effectively reduced the number of feature points extraction, and uses the direct average fusion method for low pass filter, get spliced images. The simulation results show that the algorithm can effectively solve the problems of ghost and distortion in the process of UAV image Mosaic, and the algorithm runs fast, which has certain military application value.
John Sack: Organizing the Community of Scholarly Publishing for Over 40 Years

From the early days of the World Wide Web, John Sack led HighWire Press from a fledgling technology organization based at Stanford University, to the premier content hosting platform for scientific and academic publishers. John is a scholarly publishing pioneer, innovator, and icon. In this interview, conducted by Heather Staines, Senior Strategy Consultant for Delta Think, John talks about how he became interested in technology via the old-fashioned card catalog, how Silicon Valley luminaries influenced his career path and approach to both organization and innovation, and how HighWire helped transform the scholarly publishing industry from a journal-based economy to an article-based economy. John also touches on how listening to diverse opinions can lead to innovative ideas, what he believes are the best uses for artificial intelligence (AI), and the benefits and drawbacks of remote work. This interview took place on March 30, 2023. Science Editor: You spent a substantial part of your career connected to Stanford University, but I always wondered, how did you end up there, and what were your early days like at the university? John Sack: Well, it’s probably a 40-year story, but I’ll try to make it more compact than that. I was an undergraduate at the University of Virginia in the 70s, and I was in an interesting program that allowed me to put together a mixture of studies in all sorts of areas. The upside was that it was a lot of fun, and I had some great professors, but I didn’t look like […]