Reviewerly/exHarmony huggingface dataset:
LGLFF: A Lightweight Aspect-Level Sentiment Analysis Model Based on Local-Global Features

Aspect-level sentiment analysis is highly dependent on local context. However, most models are overly concerned with global context and external semantic knowledge. This approach increases the training time of the models. We propose the LGLFF (Lightweight Global and Local Feature Fusion) model. Firstly, we introduce a Distilroberta pretrained model in the LGLFF to encode the global context. Secondly, we use the SRU++ (Simple Recurrent Unit) network to extract global features. Then we adjust the SRD (Semantic-Relative Distance) threshold size by different datasets, and use SRD to mask the global context to get the local context. Finally, we use the multi-head attention mechanism to learn the global and local context features. We do some experiments on three datasets: Twitter, Laptop, and Restaurant. The results show that our model performs better than other benchmark models.
Moving Object Detection Based on Truncated Nuclear Norm and 3D Total Variation

Robust principal component analysis (RPCA) has been widely used in moving object detection, but the traditional low rank and sparse decomposition method constrained by nuclear norm can only be applied to simple scenes such as static background. In dynamic background, the traditional RPCA method cannot extract moving objects completely, and it is easy to detect the background as moving objects by mistake. To solve this problem, a moving object detection model based on truncated nuclear norm and total variation is proposed. The model uses truncated nuclear norm to better constrain the low rank of video background; Considering the temporal and spatial continuity of moving objects, the constraint of moving objects is realized by 3D total variation, so as to improve the accuracy of moving object detection. Finally, the two-step iterative strategy and augmented Lagrange multiplier method are introduced to solve the proposed model. Through simulation experiments and comparing the F-measure values of each algorithm, it is proved that the proposed model can effectively improve the accuracy of detecting moving objects in dynamic background from the perspective of vision and quantification, and achieved better visual effect than the existing model.
Criminal Scene Investigation Image Retrieval Based on Multi-bag Multi-instance Learning Algorithm

Focus on the weak supervised learning problem in the criminal scene investigation (CSI) image retrieval, based on the random forest (RF) ensemble strategy, a novel multi-bag multi-instance learning (MB-MIL) algorithm is proposed. Firstly, by using Pyramid overlapping grid partitioning (POGP), a multi-bag multi-instance modeling scheme is designed to convert each CSI image into four different types multi-instance bags, so the CSI image retrieval problem was transformed into a MIL problem. Secondly, some discriminative instances are selected from the positive training bags by a defined criterion function to construct a discriminative instance set (DIS), and then based on the sparse coding and maximum pooling techniques, a discriminative coding feature (DCF) calculation method is proposed to convert every bag to a single sample, which allows the MIL problem to be solved directly by the RF algorithm. Finally, four RF classifiers are trained based on the four different DCFs, and they are integrated by a weighted fusion strategy to obtain the final MB-MIL classifier. Experimental results based on the CIIP-CSID-6000 and COREL1000 image sets show that the average AUC values of the MB-MIL algorithm are 7.6% and 6.0% higher than that of single-bag MIL algorithm, and its performance is superior to other MIL algorithms.
Single Image Dehazing Via Enhanced CycleGAN

Due to the influence of atmospheric light scattering, the images acquired by outdoor imaging device in haze scene will appear low definition, contrast reduction, overexposure and other visible quality degradation, which makes it difficult to handle the relevant computer vision tasks. Therefore, image dehazing has become an important research area of computer vision. However, existing dehazing methods generally require paired image datasets that include both hazy images and corresponding ground truth images, while the recovered images are easy to occur color distortion and detail loss. In this study, an end-to-end image dehazing method based on Cycle-consistent Generative Adversarial Networks (CycleGAN) is proposed. For effectively learning the mapping relationship between hazy images and clear images, we refine the transformation module of the generator by weighting optimization, which can promote the network adaptability to scale. Then in order to further improve the quality of generated images, the enhanced perceptual loss and low-frequency loss combined with image feature attributes are constructed in the overall optimization objective of the network. The experimental results show that our dehazing algorithm effectively recovers the texture information while correcting the color distortion of original CycleGAN, and the recovery effect is clear and more natural, which better reduces the influence of haze on the imaging quality.
Fine-Grained Recognition with Incremental Classes

This work focuses on dealing with fine-grained recognition problems when incremental classes emerge. The task is desirable to not only distinguish subordinate visual classes based on discriminative but subtle object parts, but also recognize new coming sub-classes without suffering from catastrophic forgetting. In this paper, we first propose to localize both object- and part-level image regions for capturing powerful fine-grained patterns. Then, these fine-grained regions are fed into a bilateral network consisting of a stable branch and a flexible branch for supporting observed and incremental sub-classes recognition respectively. Moreover, a cumulative adaptation strategy is further equipped to adjust the network training during the incremental sessions. Meanwhile, to better retain the modeling capability of observed classes, we also replay samples from previous classes by a hallucination approach. Experiments are conducted on three popular fine-grained recognition datasets and results of the proposed method can reveal its superiority over state-of-the-arts.
Closed Iterative Correction Algorithm for Multi-target Dynamic Tracking and Positioning

Aiming at the instability and low accuracy of Generalized Motion Simultaneous Localization and Mapping (GEM-SLAM), a closed iterative update dynamic tracking and location algorithm based on Generalized Labeled Multi-Bernoulli (GLMB) filter is proposed. It consists of two closed iteration modules, which are established to realize the closed-form optimal solutions of sensor node motion correction and multi-target tracking and positioning respectively. The sensor node upon correction can provide more accurate targets measurement, and multi-target tracking and positioning module can obtain stable and accurate results by using this measurement. Experimental simulation verifies the algorithm of this paper, which reflects the excellent characteristics of our algorithm.
Blockchain Fragment Expansion Scheme Based on Community Detection and MSR Code

Aiming at the current performance of blockchain system can not meet the actual needs of high-frequency data interaction in the era of information explosion, a blockchain fragment expansion scheme based on community detection and MSR code is proposed. Firstly, based on the random walk community detection algorithm and the blockchain node network, a node grouping method is proposed. Each group of nodes maintains a copy of the blockchain ledger to reduce the storage redundancy of blockchain. Secondly, the block data is divided by minimum storage regenerating codes (MSR), which further reduces the storage pressure of nodes, reduces the amount of data required for block requests, and reduces network pressure. Finally, a blockchain scalable fragment storage scheme is proposed. The simulation experiment and result analysis show that the blockchain fragment expansion scheme based on community detection and MSR has improved in terms of storage scalability, response efficiency and fault tolerance.
An Augmented Reality Tracking Registration Method Based on Deep Learning

Augmented reality is a three-dimensional visualization technology that can carry out human-computer interaction. Virtual information is placed in the designated area of the real world to enhance real-world information. Based on the existing implementation process of augmented reality, this paper proposes an augmented reality method based on deep learning, aiming at the inaccurate positioning and model drift of the augmented reality method without markers in complex backgrounds, light changes, and partial occlusion. The proposed method uses the lightweight SSD model for target detection, the SURF algorithm to extract feature points and the FLANN algorithm for feature matching. Experimental results show that this method can effectively solve the problems of inaccurate positioning and model drift under particular circumstances while ensuring the operational efficiency of the augmented reality system.
Reversible Data Hiding Algorithm for Binary Image Based on Sliding Window

Since binary images are only black and white, it is prone to produce visual distortion when embedding data. And due to the two modes of binary images, the image can store less information when hidden, and the embedded capacity is generally small. Some existing binary image reversible data hiding algorithms using image segmentation methods do not make full use of the pixels. Given the above problem, a binary image reversible data hiding algorithm based on sliding window is proposed. This algorithm eliminates the influence of different binary image content factors on the embedding capacity to a certain extent, improves the embedded capacity and visual quality. Experimental results show that the embedded capacity of different types of binary images is significantly improved. Among them, the embedding capacity of CAD reaches 2090 bits ,which is increased by 6.5 times. Word document reaches 1407bits, which is increased by 3.45 times. The PSNR of the binary images is increased significantly, the average PSNR can reach 32.74dB, which is increased by 2.19dB, of which the average PSNR of Word documents is 33.27dB, which is increased by 3.7dB.
Lightweight Transformer Tracker: Compact and Effect Neural Network for Object Tracking with Long-Short Range Attention

Recent years, attention mechanism has been widely used in computer vision, such as object detection, tracking and recognition. Many studies reveal that attention has better performance than those CNN-based or RNN-based networks. However, the cost of better performance, such as complicated structure and complex algorithm, cannot be neglected. In this paper, a lightweight transformer tracker named LTT is proposed. Different from Transformer Tracking network (TransT), three aspects of lightweight operation are adopted: firstly, YOLO-nano Darknet is utilized as the feature extraction network; secondly, the size of template image is scaled to 1/4 of the original and removing the self-attention layer; finally, the combination layer of convolution and cross-attention (long-short range attention) is adopted for the sake of feature fusion. Experiments show that our tracker runs at roughly 70 fps on GPU while there is no significant performance loss compared with other networks like SiamFc or SiamRPN++. Moreover, the model size of our tracker is only 4.58M.
Visual Question Answering Model Based on CAM and GCN

Visual Question Answering (VQA) is a challenging problem that needs to combine concepts from computer vision and natural language processing. In recent years, researchers have proposed many methods for this typical multimodal problem. Most existing methods use a two-stream strategy, i.e., compute image and question features separately and fuse them using various techniques, rarely relying on higher-level image representations, to capture semantic and spatial relationships. Based on the above problems, a visual question answering model (CAM-GCN) based on Cooperative Attention Mechanism (CAM) and Graph Convolutional Network (GCN) is proposed. First, the graph learning module and the concept of graph convolution are combined to learn the problem-specific graph representation of the input image and capture the interactive image representation of the specific problem. Image region dependence, and finally, continue to optimize the fused features through feature enhancement. The test results on the VQA v2 dataset show that the CAM-GCN model achieves better classification results than the current representative models.
An Improved Two-dimensional Sine Cosine Map and Its Performance Analysis

Aiming at the problems of small parameter interval, simple chaotic behavior and poor initial value sensitivity of traditional one-dimensional chaotic map, a one-dimensional fractional chaotic system is improved by using the combination of Sine and Cosine map. On this basis, a Sine Cosine two-dimensional chaotic map is further proposed. Through the experimental simulation of the Lyapunov exponent, bifurcation diagram, initial value sensitivity and correlation of the chaotic system, the results show that the proposed two-dimensional chaotic map has larger chaotic parameter interval, better chaotic behavior and initial value sensitivity.
Effective Training-Time Stacking for Ensembling of Deep Neural Networks

Ensembling is a popular and effective method for improving machine learning (ML) models. It proves its value not only in classical ML but also for deep learning. Ensembles enhance the quality and trustworthiness of ML solutions, and allow uncertainty estimation. However, they come at a price: training ensembles of deep learning models eat a huge amount of computational resources. A snapshot ensembling collects models in the ensemble along a single training path. As it runs training only one time, the computational time is similar to the training of one model. However, the quality of models along the training path is different: typically, later models are better if no overfitting occurs. So, the models are of varying utility. Our method improves snapshot ensembling by selecting and weighting ensemble members along the training path. It relies on training-time likelihoods without looking at validation sample errors that standard stacking methods do. Experimental evidence for Fashion MNIST, CIFAR-10, and CIFAR-100 datasets demonstrates the superior quality of the proposed weighted ensembles c.t. vanilla ensembling of deep learning models. The suggested approach spends on training of N models N times less time than classical ensemble and provides significantly higher quality compared to the single model.
An Error Analysis of Using Simple Past Tense in Writing Narrative Text at Tenth Grade Students of Senior High School 1 Jambi

This study aims to identify whether students make errors and find the types of errors made by students in class X MIPA 1 of Senior High School 1 Jambi City. This research is descriptive qualitative. The subject of this research is X MIPA 1 of Senior High School 1 Jambi City academic year 2021/2022. The data collected through the students' writing test. The results of this study indicate that there are errors made by students in the use of the past tense. All errors in using the past tense have been categorized and identified, namely omission, addition, misformation, and misordering. The findings of this study indicated that there were 102 total errors made by students which are classified into four categories based on the surface strategy taxonomy. There are 14 omission errors, 27 addition errors, 49 forming errors, and 12 sorting errors. The students made a dominant error in misformation. This can be seen from the results of the percentage where the wrong formation is 48.04%, then followed by stacking errors of 11.76%, omissions of 13.73, and addition errors of 26.47%. Based on the results of the study, it can be concluded that the students of class X MIPA 1 SMAN 1 Jambi City made the most types of errors, namely misformation. This misformation error is the most difficult for students because they do not really understand the movement of verbs and the infinitive to the past tense. Students need to practice more in writing narrative texts so that they understand the use of simple past tense and the students can make good narrative texts.
A Method with Universal Transformer for Multimodal Sentiment Analysis

Multimodal sentiment analysis refers to the use of computers to analyze and identify the emotions that people want to express through the extracted multimodal sentiment features, and it plays a significant role in human-computer interaction and financial market prediction. Most existing approaches to multimodal sentiment analysis use contextual information for modeling, and while this modeling approach can effectively capture the contextual connections within modalities, the correlations between modalities are often overlooked, and the correlations between modalities are also critical to the final recognition results in multimodal sentiment analysis. Therefore, this paper proposes a multimodal sentiment analysis approach based on the universal transformer, a framework that uses the universal transformer to model the connections between multiple modalities while employing effective feature extraction methods to capture the contextual connections of individual modalities. We evaluated our proposed method on two benchmark datasets for multimodal sentiment analysis, CMU-MOSI and CMU-MOSEI, and the results outperformed other methods of the same type.
A Novel Global-Local Representations Network for Speech Steganalysis

With the popularity of the internet and the development of network services, Steganography based on speech stream has become a research hotspot in information hiding. To improve the detection performance of steganalysis of multiple steganography methods, in this paper, we proposed the Global-Local Representations Network (GLRN), which consists of a Global Correlation Extraction (GCE) module and a Local Correlation Enhancement (LCE) module. Firstly, considering the inter-class differences of different coding elements, the GCE module is used to capture the global correlation of different coding elements by using multi-channel modeling. Then, we realize that the process of global correlation extraction suffers from the loss of detailed information, so the LCE module is used to capture local correlations to complement the global features. The experiments show that the GLRN achieves the start-of-art detection performance.
An Analysis of Barrier Factors in Teaching and Learning English at the Ninth Grade Student of SMP YKP Pertamina Jambi

This research was aimed to find out the barrier factors in teaching and learning English at the ninth grade students of SMP YKP Pertamina Jambi. The research methodology of this study was qualitative research with random sampling. The researcher conducted at ninth grade students in SMP YKP Pertamina Jambi. The subject of this research is 1 teacher and 7 students, by using online interview with research instrument, technique of data collecting, and technique of data analysis. Overall, the result of this research showed barrier factors in teaching and learning English in SMP YKP Pertamina Jambi. Finding from this research shows that there are barrier factors when teaching and learning English, and factors that most influence students when learning English. Hence, it can be concluded that there was a barrier factor which was the teacher's mistake when learning English and the most dominant factor experienced by students was family factors that could affect in learning.Keywords: Barrier factors, teaching, learning process.
Pre-primary CLIL and STEAM: Science in the Early Years

This chapter opens with a reference to important characteristics related to Pre-primary children and the way they learn. One of those characteristics has to do with the fact that very young learners are said to be natural born scientists. Given that scientific knowledge and skills have become increasingly important in today’s societies, fostering an interest in pure science and developing scientific literacy in these learners can make a difference in specialized workers of tomorrow. STEM Education and STEAM (sustained on Science, Technology, Engineering, Maths, plus Arts), prove to fit that aim. That is why the chapter focuses on STEAM as a methodological approach that promotes active learning by exploiting contents from the areas in the acronym in combination with the resolution of everyday problems through experiments and other meaningful activities. Its own characteristics prove its suitability for Pre-primary stages, where it can be put into practice in CLIL lessons. Ways of doing that are proposed by resorting to scientific contents related to simple experiments and the exchange of questions and answers, descriptions, or predictions within settings that facilitate interaction with the social and natural environment and foster cognitive development and scientific skills among very young learners.
Automated Synthesis of Software Contracts with KindSpec

In this paper, we describe KindSpec, an automated tool that synthesizes software contracts from programs that are written in a significant fragment of C that supports pointer-based structures, heap manipulation, and recursion. By relying on a semantic definition of the C language in the $$\mathbb {K}$$ semantic framework, KindSpec leverages the symbolic execution capabilities of $$\mathbb {K}$$ to axiomatically explain any program function. This is done by using observer routines in the same program to characterize the program states before and after the function execution. The generated contracts are expressed in the form of logical axioms that specify the precise input/output behavior of the C routines, including both general axioms for default behavior and exceptional axioms for the specification error behavior. We summarize the main services provided by KindSpec, which also include a novel refinement facility that improves the quality and accuracy of the synthesized contracts. Finally, we provide an experimental evaluation that assesses its effectiveness.
Optimizing Maude Programs via Program Specialization

We develop an automated specialization framework for rewrite theories that model concurrent systems. A rewrite theory $$\mathscr {R}=(\Sigma ,E\uplus B,R)$$ consists of two main components: an order-sorted equational theory $$\mathscr {E}=(\Sigma ,E\uplus B)$$ that defines the system states as terms of an algebraic data type and a term rewriting system R that models the concurrent evolution of the system as state transitions. Our main idea is to partially evaluate the underlying equational theory $$\mathscr {E}$$ to the specific calls required by the rewrite rules of R in order to make the system computations more efficient. The specialization transformation relies on folding variant narrowing, which is the symbolic operational engine of Maude’s equational theories. We provide three instances of our specialization scheme that support distinct classes of theories that are relevant for many applications. The effectiveness of our method is finally demonstrated in some specialization examples.