Reviewerly/exHarmony huggingface dataset:
Facial expression classification based on LDA-GS-SVM

With the development of artificial intelligence (AI), facial expression recognition has become a focus of current research owing to its wide application potential. In this study, based on the karolinska directed emotional faces KDEF face public dataset, we adopted a parameter optimization method to optimize the kernel function parameters of the support vector machine which was used for classification. This study also conducted a comprehensive comparative analysis of the effects of expression recognition before and after parameter optimization. First, traditional manual and deep learning methods were used to extract face features, form two feature sets, and combine them to obtain a hybrid feature set. Second, linear discriminant analysis was used to reduce dimensionality, and a correlation matrix was used to select the class vectors to remove redundant features. Subsequently, grid search and k-fold cross-validation methods were used to optimize the parameters. The kernel function with default parameters and the parameter-optimized kernel function were used to classify the feature sets, and the effects of parameter optimization on facial expression classification were compared and analyzed. Finally, the classification effect of support vector machines with different parameters was evaluated. The results show that the overall classification effect of support vector machine is good and that parameter optimization can improve the accuracy of expression classification to a certain extent. The classification accuracy of the test set for the parameter-optimized support vector machine can be as high as 98.8%, indicating good applicability to the dataset.
Lqscomp: an efficient quality score compression method based on run length coding and mapping

With the continuous development of high-throughput sequencing technology over the past two decades, the cost of gene sequencing has fallen sharply, resulting in the rapid growth of genetic data. Therefore, it is urgent and important to compress large scale DNA data effectively. The core of DNA data compression is the compression of the base sequence and the quality score sequence. Base compression has made great progress in recent years, but quality score compression is still challenging. In this paper, a lossless compression tool Lqscomp for quality scores is proposed. It includes four steps: partitioning, indexing, mapping and compression. The feature of the tool is that the mapping step uses a technology similar to run-length encoding. Experimental results show that the proposed Lqscomp algorithm has a good compression performance on all test sets.
Research on English speaking assessment algorithms based on deep learning

The mainstream deep learning spoken language evaluation algorithms are based on speech recognition to perform mispronunciation determination. In the process of speech recognition, Deep Neural Network-Hidden Markov Model (DNN-HMM) has better performance than Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). In this paper, the structure of speech recognition system is analyzed and the current mainstream English speaking evaluation algorithms are analyzed, and it is concluded that the current mainstream evaluation algorithms are basically not end-to-end and have low evaluation metrics. Therefore, this paper designs an end-to-end speech evaluation model based on the theory of deep learning, and conducts experiments for this model under the TIMIT speech dataset to verify and analyze the advantages of this paper's model in temporal modeling, which shows that the speech recognition model designed in this paper has better performance in spoken language evaluation.
A knowledge graph representation learning based approach to qualitative case discipline

To solve the problem of irregular and inefficient case characterization and discipline in the field of discipline inspection and supervision, a method of case characterization and discipline measurement based on knowledge map representation learning is proposed. The method uses the knowledge map of discipline inspection cases as auxiliary information, and the knowledge representation learning model is used to characterize the entities and relationships in the knowledge graph, and the results of the characterized representation are subsequently processed by similar case calculation, and then uses the results of the representation to calculate similar cases to further realize the qualitative discipline of cases. The negative sampling method of the knowledge representation learning model is investigated to improve the knowledge graph vectorization accuracy and facilitate the manipulation of the knowledge graph in the case of qualitative quantitative discipline task. The knowledge graph inference-based approach to case characterization and discipline improves accuracy and computational efficiency. By improving the existing model, the improved model can reduce the impact of noisy data to a certain extent.
Where the TinyPerson escapes: SOLEnet small object location estimates from coarse to fine

Existing object detection methods primarily focus on detecting large targets in images, with limited research conducted on small targets. Additionally, there are challenges with low detection accuracy and difficulty in meeting real-time requirements. Based on this, an improved RetinaNet is proposed. First, the use of high-resolution feature maps can better capture the features of small targets. Then a weight factor is added to solve the unbalanced distribution of training samples caused by using high-resolution feature maps. Finally, adding a novel query subnet by two main steps: first estimate the coarse location of the small target in the high-level feature map, then the exact location is calculated in the low-level feature map. Experiments show that this query subnet saves the computational cost of low-level feature maps, and greatly improved detection speed. The performance of the model is evaluated on a new benchmark called TinyPerson, mAP up to 36.18, FPS up to 13.98. Compared to the unimproved RetinaNet, the accuracy and speed are improved by 0.9 and 1.28 respectively.
An incremental object detection model based on knowledge distillation

This paper proposes an incremental object detection model framework based on knowledge distillation. The framework addresses the issue of catastrophic forgetting by optimizing the distillation loss function and expanding the labels of the new dataset based on the old object classes, which significantly enhances the recognition capability of the model for the old objects. The framework is implemented and evaluated on the two-stage object detection algorithm. Experimental results on the PASCAL VOC dataset demonstrate that the proposed model achieves competitive performance compared to state-of-the-art methods.
A point cloud optimization algorithm based on nonadjacent low-altitude remote sensing images

This paper presents a robust point cloud optimization algorithm based on nonadjacent low-altitude remote sensing images. The proposed algorithm is designed to optimize the performance of point cloud generation in accuracy and efficiency. In order to accelerate the process of stereo matching and compute the coordinate of object point accurately, a pair of nonadjacent images is employed to lengthen the epipolar line of the image pair. Then a patch based Least Square Matching (LSM) method is utilized to search the optimal matching pixels and compute the coordinates of corresponding object points in 3D space. Comparison studies and experimental results in point cloud generation about low altitude remote sensing images have proved the effectiveness of the proposed algorithm.
Collusion resistant identity-based proxy re-encryption scheme on lattice

Proxy re-encryption plays an important role for data security in cloud computing and big data, but the traditional re-encryption scheme based on the classical number theory problem cannot resist quantum attack, and the lattice-based cryptosystem has been proved to have the ability to resist quantum attack, so the lattice-based re-encryption scheme has always been a research hotspot. In view of the problem that collusion-resistance and multi-hop cannot be satisfied at the same time in existing schemes, this paper proposes a new identity-based proxy re-encryption scheme on lattice. Firstly, the scheme uses trapdoors as private keys to achieve one-way anti-collusion. In addition, we construct a variant of Learning with Errors (LWE), which uses a convert algorithm to transform the user matrix and replace the vector with a uniform matrix, so that the scheme can meet the requirements of multi-hop and encrypt multiple bits of messages at one time. Theoretical analysis and experimental simulation prove that the security of the new scheme can be reduced to a decision LWE problem, which can reach the IND-sID-CPA security in the standard model and can improve the encryption efficiency while ensuring the security.
Image stitching method based on asphalt pavement potholes

An asphalt road pothole image stitching algorithm is proposed to address the issues of low feature point matching accuracy, loss of pothole disease information, ghosting and deformation of the pothole when using traditional image stitching algorithms. The goal is to stitch the images captured by the pavement image acquisition equipment to form a complete asphalt pavement picture with the disease information of the pothole area intact. The stitched image's pixel point coordinates are transformed to the coordinates of the reference image using a single-strain matrix after first extracting the pit regions in the image and extracting feature points for these regions using the MSER-SIFT feature extraction algorithm. Next, the best matching point pairs are found using an improved random sampling consistency algorithm. Finally, a region-weighted averaging approach is utilized to merge the images. The experimental asphalt road images include pavement images with sufficient light during the day, pavement images with complete pothole disease information, pavement images with incomplete potholes, and pavement pothole images at night to test the effectiveness of pavement stitching under various conditions. The research results show that this splicing method not only improves the matching accuracy and speed of the traditional splicing algorithm, but also ensures the integrity and invariance of the pavement pothole information.
Short text classification model based on dynamic routing and CNN with attention mechanism

Unlike long texts, short texts can cause the problem of feature sparsity due to their short length. Although the existing deep learning-based methods alleviate this problem, the extracted semantic features are inevitably redundant, and the relationships between features are not fully considered when performing feature fusion, which in turn makes the semantics ambiguous and classification very difficult. To address the multi-feature fusion problem, we propose a Short Text Classification Model Based on Dynamic Routing and CNN with Attention Mechanism (DCAN). Firstly, we use Convolutional Neural Network (CNN) to extract text features with different granularity to enrich the semantic representation; secondly, we use attention mechanism and residual structure to fuse features with different granularity to obtain features with contextual semantic relationships, and then them into dynamic routing to obtain the probability distribution of short texts. We conducted experiments on four datasets, including AG News, MR, TREC, and SST-2. The experimental results show that DCAN has higher classification accuracy compared with most of the currently popular models.
Research on key technologies of speech recognition based on deep learning

The research on speech recognition shows that the accuracy of speech recognition in practical application scenarios is still not high because speech is affected by the user's unclear pronunciation, environmental noise and other factors. A key technology of speech recognition based on deep learning is proposed for resource browsing in campus enrollment. Firstly, the computational advantages of deep learning and the problems in the process of audio acquisition are analyzed. FFT algorithm is proposed to complete the noise reduction and enhancement of speech, and LSTM algorithm to complete the enhancement and recognition of speech. The experimental results show that FFT+LSTM algorithm has a high accuracy of speech recognition, reduces the response time of speech recognition, and has better flexibility and practicability.
Architecture design and key technology analysis of military logistics cloud platform

Cloud computing has obvious advantages of flexible deployment, strong scalability and high security. Using cloud computing technology can effectively integrate our existing logistics information systems and information resources, establish a comprehensive, advanced, convenient deployment, stable and reliable military logistics cloud platform. It can improve our information system's comprehensive service capabilities in information acquisition, storage, integration and processing, improve the situation of low compatibility between existing platforms and systems. Based on the main business of logistics support and cutting-edge theory, this paper designs the overall functional and technical architecture of the military logistics cloud platform. It completed the design of the cloud platform management layer, security control layer, and functional implementation layer, with a focus on analyzing and designing the IaaS, PaaS, and SaaS layers. From the perspective of cloud platform construction, the selection of key technologies such as virtual machine isolation technology and dynamic resource scheduling is demonstrated. This study can provide a useful reference for the construction and development of our military logistics cloud platform and logistics informatization in the new era.
Pose and attention mechanism based behavior recognition method and its application in education

Behavior recognition is an important research domain in computer vision. Extracting human motion features and identifying behaviors from video can provide important support for education, medical treatment, security, and other fields. However, current mainstream behavior recognition methods prove ineffective in the challenging context of recognizing students’ classroom behavior due to scene complexity, numerous interfering factors, and lack of public datasets. To address these limitations, this paper presents a novel recognition model that integrates behavioral pose information and attention mechanisms. The proposed model employs a residual convolutional neural network with an attention mechanism to extract spatial and temporal features from teaching videos while leveraging behavioral pose information to enhance the description of visual cues and capture high-level behavioral information embedded in depth features. To assess the model's effectiveness, we create a comprehensive dataset of students' classroom behavior and conduct extensive experiments. The results demonstrate the proposed model's high performance in recognizing college students' classroom behavior.
Design and verification of AES encryption algorithm based on hardware agile development

The AES encryption algorithm is widely used in various fields, such as network data security, database encryption and file data transmission, due to its high reliability, real-time performance, and flexibility. However, existing hardware implementation methods, such as FPGA and dedicated encryption chips, have the disadvantages of low development efficiency and poor flexibility. To solve this problem, this article proposes a hardware agile development approach combined with the Chisel language to implement the AES encryption algorithm for typical cryptographic applications. The algorithm's interface, including row shifting, key expansion, and key round adding, is designed using the Chisel language. Functional modules and corresponding module test codes are implemented. V erilog code is generated using the Chisel language and successfully deployed on hardware. This agile design and verification approach reduced the code development workload by 55% while ensuring that the encryption effect and hardware resource utilization are basically consistent. The AES encryption algorithm has been successfully implemented.
A method to web application taint analysis by computing data dependency

As software plays an increasingly critical role in production life, software security issues are gaining more and more attention. Static taint analysis technology is one of the most widely used information flow analysis techniques, and the simulation of the dynamic characteristics of the detected objects has been a difficult point of research. In this paper, we propose a construction scheme based on demand-driven data dependency calculation for the dynamic response problem of Web back-end application processor method, and simulate the invocation relationship that cannot be revealed on the static control flow graph. The solution was then implemented with the help of Soot framework to perform SQL injection detection for web back-end applications and verified in comparison with the current detection tools.
Multi-modal assisted knowledge distillation for 3D question answering

3D question answering (3D-QA) aims to answer free-form nature language questions given 3D scenes represented by point clouds. Compared to traditional 2D-QA, 3D-QA poses a dual challenge for models by assessing their understanding of both object appearance and structure, along with their spatial relationships. In this work, we introduce a novel method, named M2AD, that leverages multi-modal data to enhance the representation of 3D scene point clouds during the training phase. Specifically, we augment the capabilities of the model by incorporating 2D features corresponding to 3D objects and captions corresponding to the scene into the 3D object proposal stage, thereby endowing it with stronger representation abilities. Furthermore, to ensure self-reliance during inference without the need for additional data, we adopt a teacher-student framework to distill the enhanced model's knowledge to a model solely utilizing point cloud data. Extensive experimentation substantiates the effectiveness of our proposed model.
Measuring cognitive load in augmented reality with physiological methods: A systematic review

Abstract Background Cognitive load during AR use has been measured conventionally by performance tests and subjective rating. With the growing interest in physiological measurement using non‐invasive biometric sensors, unbiased real‐time detection of cognitive load in AR is expected. However, a range of sensors and parameters are used in various subject fields, and reported results are fragmented. Objectives The aim of this review is to analyse systematically how physiological methods have been used to measure cognitive load and what the implications are for the future research on AR‐based tools. Methods This paper took the systematic review approach. Through screening with 10 exclusion criteria, 23 studies, that contain 3 key elements: AR‐based intervention, cognitive state examination and physiological methods, were identified, analysed and synthesised. Results Physiological methods in their current form require reference to provide meaningful interpretations and suggestions. Therefore, they are often combined with conventional methods. Many studies investigate the effect of wearable devices in comparison with non‐AR stimuli, which has been controversial, but detection of different causes of cognitive load are on the horizon. Eye‐tracking is the method most used and most consistent in the use of its parameters. Conclusions A multi‐method approach combining two or more evaluation instruments is essential for the validation of users' cognitive state. In addition to the AR stimuli in question, having another independent variable such as task difficulty in experiment design is useful. Statistical approaches with more data input could help establish a reliable scale. The future research should attempt to dissociate cognitive load caused by different effects such as device, instruction, and other AR techniques as well as intrinsic and extraneous aspects, in a better experimental setup with multiple parameters.
Leo Marks’ “mental one-time pad”: an experimental cipher for British special operations in World War II

AbstractDuring World War II, Leo Marks designed ciphers for the British Special Operations Executive (SOE). Tasked with ensuring that SOE undercover agents could encrypt radio transmissions from Nazi-occupied Europe to British headquarters, Marks introduced one-time pad ciphers with random keys printed on materials like paper and silk. However, carrying such materials was risky for agents. In an attempt to mitigate this risk, Marks experimented with a system he called the “Mental One-Time Pad” (MOP) cipher. Contrary to its name, the MOP cipher was not a genuine one-time pad system, but rather used double columnar transposition with memorized texts to create unique substitution keys. In this article, we’ll explore the design, vulnerabilities, and historical significance of the MOP cipher, and discuss potential improvements from US Army cryptographer William Friedman.Keywords: Leo Marksmental one-time pad (MOP)transposition-substitution ciphersWilliam FriedmanWorld War II ciphers Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationNotes on contributorsThomas LarsenThomas Larsen is an Australian data scientist in the commercial sector as well as in critical care research at the University of Melbourne and Monash University. He specializes in accurate and interpretable (A+I) machine learning techniques including symbolic regression.
Research and application of image inpainting based on vision transformer and generative adversarial networks

Image inpainting techniques have gradually been replaced by deep learning-based methods from traditional methods based on structure and texture, and the effectiveness has been improved. However, there are still many issues, such as the problem of blurring, color distortion, and inconsistent semantic information with the intact region in the restored image. This paper proposes a method that combines Vision Transformer and Generative Adversarial Networks, and trains the model with large-scale masks to enable the Transformer encoder to learn deep-level feature information as much as possible. Traditional convolution cannot make the model dynamically distinguish valid information from invalid information. In this method, gated convolution is added to the generator to mask out invalid information in missing areas. Compared with the traditional attention model, the method proposed in this paper has better structural texture and semantic consistency in the inpainting effect.