Reviewerly/exHarmony huggingface dataset:
Preprocessing Low Quality Handwritten Documents for OCR Models

Abstract: Handwriting recognition using OCR (Optical Character Recognition) is a transformative technology that is rapidly changing the way we interact with handwritten documents. OCR technology has traditionally been used for scanning printed text, but with advancements in machine learning and computer vision, it is now possible to recognize and digitize handwritten text as well. This has immense practical implications, as it enables handwritten notes, letters, and documents to be easily searchable, editable, and shareable in digital formats[1]. However, despite its potential, handwriting recognition using OCR is still a relatively nascenttechnology and faces several challenges. One of the main challenges is the recognition of handwriting styles that vary widely across individuals and cultures. Another challenge isinterpreting handwriting that is difficult to read, such as cursive writing or handwritten notes withsmudges or crossed out words. Additionally, OCR software is highly dependent on the qualityof the input image, making it important to optimize the lighting and capture settings for accurate results.[2] As OCR technology continues to improve, it hasthe potential to revolutionize the way we interact with handwritten documents and usher in a new era of digital transformation.
Integrating Collaborative ICT Tools in Higher Education for Teaching and Learning

In this chapter, the author will discuss his experiences working collaboratively to teach and learn in order to create an engaged pedagogy by subscribing action research methodologies in various semesters that involves his (PGD, Master, and MPhil) students of 2019-2021 batches in techno-pedagogy and its trend in learning. This chapter presents the ongoing learning of research conducted within the context of the researcher's teaching practice. The author will provide examples of some of his PGD, Master, and MPhil students' technological learning collaborative activities (i.e., LMS, Google Apps, and other open sources), including forum discussions, choices, managing quizzes, lesson study, workshops, and Google Docs activities (Doc, PowerPoints, and Jamboard), which are created for on-campus, online, and distance teaching and learning in Nepali universities (namely, Kathmandu University and Nepal Open University).
Factors that influence students’ acceptance of mobile learning for EFL in higher education

Using mobile learning (m-learning) to study English as a foreign language (EFL) has provided new opportunities for innovative learning, particularly for today’s younger generation who have grown up with digital technology. It can effectively promote teaching and learning by maintaining students’ engagement. This study aimed to investigate the factors that influence university students’ acceptance of m-learning for EFL in Taiwan. A survey questionnaire was administered to 327 participants, and the data was analyzed using descriptive statistics, t-tests, one-way ANOVA, and Pearson’s correlation. The results indicated that all factors received relatively high scores, with perceived ease of use, attitude toward use receiving the highest scores. This suggests that students widely accept m-learning as a means of studying EFL. The key to creating a successful m-learning experience is for students to accept it as an educational tool and be willing to use it. Therefore, it is recommended that m-learning be considered as a potential tool to improve EFL students’ language proficiency.
Viewpoint-Adaptive Representation Disentanglement Network for Change Captioning

Change captioning is to describe the fine-grained change between a pair of images. The pseudo changes caused by viewpoint changes are the most typical distractors in this task, because they lead to the feature perturbation and shift for the same objects and thus overwhelm the real change representation. In this paper, we propose a viewpoint-adaptive representation disentanglement network to distinguish real and pseudo changes, and explicitly capture the features of change to generate accurate captions. Concretely, a position-embedded representation learning is devised to facilitate the model in adapting to viewpoint changes via mining the intrinsic properties of two image representations and modeling their position information. To learn a reliable change representation for decoding into a natural language sentence, an unchanged representation disentanglement is designed to identify and disentangle the unchanged features between the two position-embedded representations. Extensive experiments show that the proposed method achieves the state-of-the-art performance on the four public datasets. The code is available at https://github.com/tuyunbin/VARD.
Anticipatory analysis of AGV trajectory in a 5G network using machine learning

Abstract A new generation of Automatic Guided Vehicles (AGV) virtualises their Programmable Logic Controller (PLC) in the cloud deploying 5G-based communication infrastructures to provide ultra-fast and reliable links between the AGV and its PLC. Stopping an AGV can result in a loss of tens of thousands of euros per minute and therefore, the use of machine learning techniques to anticipate AGV behavior seems to be appropriate. This work proposes the application of advanced deep neural networks to forecast AGV trajectory errors even if disturbances appear in the 5G network by capturing the packets of the PLC-AGV connection and not using any sensor in the user equipment (AGV or PLC), which facilitates the real-time deployment of the solution. To demonstrate the proposed solution, an industrial AGV and a virtualised PLC were deployed in a real 5G network. Furthermore, a set of advanced deep learning architectures was selected, and an extensive collection of experiments was designed to analyse the forecasting performance of each architecture. Additionally, we discuss the real-time issues that appeared during the execution of the best models in a 5G open laboratory, that provided a realistic deployment in a controlled scenario.
Energy-and-perception-aware planning and navigation framework for unmanned aerial vehicles

This paper presents an energy and perception aware framework for path planning and navigation of unmanned aerial vehicles (UAVs) in GNSS-denied and spatiotemporal wind environments. The proposed framework mainly consists of the global and local path planning methods that respectively consider the energy consumption of an UAV and perception quality of a light detection and ranging (LiDAR) sensor mounted on the UAV. The energy consumption is estimated based on the aerodynamic model that calculates drag and lift forces on the UAV. The global planner then uses the total energy consumption in the spatiotemporal wind as the cost function to find an energy-efficient path as a set of waypoints. The local path planning navigates the UAV between the waypoints with maintaining the perception quality. The perception quality is quantified based on how well the LiDAR sensor scans feature points around the UAV that highly correlates with the navigation accuracy. Numerical simulation study for each of the global and local path planners validates their usefulness. Further, the overall framework is entirely verified in a long-range flight scenario of the photorealistic environments developed in the Gazebo simulation.
Designing a Secure Robust Medical Image Authentication Based on Watermarking Using the ED-DWT and Encryption

The COVID-19 scenario, there was a massive increase of medical imaging data. Designing an authentication technique for safeguarding medical photographs is a difficult task. This research intended to create a hybrid authentication system that combines image encryption with a durable watermarking mechanism. The invisibility of the watermark and its resistance to attacks are the two most important requirements for watermarking. The sensitivity of encryption algorithms in the presence of noise is also discussed in the paper. For watermark embedding, the paper presented edge detection (ED) of the discrete wavelet transform (DWT) coefficient. The first-level DWT coefficients are deconstructed from the enlarged medical image. To maintain invisibility, the edge coefficients are calculated over the high-frequency DWT sub-band. Objective of this paper is to safeguard the watermark image with quick and simple image encryption. For image encryption, a random key is created. The efficiency of encryption is also tested over noisy attack for the PSNR and NC.
Hybridized Support Vector Machine and Adaboost Technique for Malaria Diagnosis

One of the most prevalence diseases in developing countries in recent years is still Malaria with global public health concern. Malaria is caused by mosquitoe's parasites which are very common in developing nations. This reason was not far from environmental insanity coupled with inadequate healthcare facilities. Thus, develop a reliable and efficient classification model for early diagnosis and discovery of the malaria symptoms is required. Hence, to reduce malaria endemic globally, the use machine learning models is essentials and paramount. Therefore, this paper proposes malaria diagnosis model using hybrid Support Vector Machines (SVM) and Adaboost. SVM classifiers for malaria classification and eliminating redundant or extraneous features were extracted using Chi-square. The classification accuracy of SVM and Adaboost models gave 97% with the six features removed but from seventh feature the accuracy reduced to 91%. The study revealed that, the developed hybridized model gives optimal solutions by using the most prominent features (symptoms) for malaria classification.
Pixel Interpolation Followed by Prediction Error Expansion-Based Reversible Information Hiding Algorithm for Securing Healthcare Data

Reversible information hiding is a type of data concealing in which the cover picture can be retrieved after the embedded data has been extracted. We presented an information concealing strategy based on linear interpolation and prediction error expansion in this research for securing healthcare-related information. A well-predicted technique for the pixels is required for the supremacy of a prediction error expansion (PEE)-based information hiding system. The picture is recursively decomposed into triangles for forming a B-tree structure for the data embedding. The suggested prediction technique uses the vertices for concealing the private bits. The planar linear interpolation of the vertex pixels is anticipated for every pixel within the triangle or on its sides. By increasing the prediction error, private data bits are inserted in pixels. Furthermore, according to the findings of the experiments, two bits can be hidden within a single pixel. Extensive experiments demonstrate the suggested method’s higher performance.
Multi-view Stereo 3D Reconstruction Algorithm Based on Improved PatchMatch Algorithm

In order to alleviate the problems of large memory consumption, low efficiency and high hardware requirements in previous deep learning-based 3D reconstruction schemes, this paper proposes an multi-view geometric 3D reconstruction network framework based on the improved PatchMatch algorithm. It consists of three parts: multi-scale feature extraction, improved PatchMatch and space optimization module. In this paper, the input view is first passed through a multi-scale feature extractor to generate three-scale features, and then the PatchMatch iterative optimization is performed on the feature maps of each scale to generate a depth map. By optimizing in the initialization phase of PatchMatch, an adaptive module is added in the propagation and cost evaluation phase of the algorithm, and the structural consistency loss function is used as the loss item of training; then the optimization algorithm model is embedded into the coarse to in a refined frame. Experimental results show that the proposed scheme can guarantee better reconstruction effect, improve reconstruction efficiency and reduce running memory.
Research on Road Object Detection Algorithm Based on Improved YOLOX

Target detection is one of the important branches of autonomous driving, where road targets are detected to achieve awareness of the surrounding environment and an efficient and accurate detection model is required to improve driving safety. The current target detection algorithm can have the problem of missing detection for obscured targets and small targets. In this paper, the YOLOX target detection algorithm is improved to enhance the recall rate of road targets. In this paper, DecIoU loss function is proposed to optimise the bounding box regression process, and Push Loss is used to improve the target occlusion problem. Finally, in order to improve the accuracy of the labels and optimise the model training process, this paper uses a dynamic anchor frame mechanism to adjust the confidence label values. Experiments are conducted with the KITTI autonomous driving dataset, and the experiments show that the method in this paper provides an effective improvement in the detection accuracy of road targets, with the mAP and mAR of YOLOX-s reaching 88.7% and 90.1% respectively, an improvement of 2.54% and 3.21%. The performance on other versions of YOLOX is also consistent, validating the effectiveness of the method in this paper.
Dynamic-YOLOv5: An improved aerial small object detector based on YOLOv5

Usually poor visual characteristics of ground targets, a lot of noise, complex backgrounds, drastic changes in target scales and high object densities pose great challenges to UAV complex ground object detection. A new dynamic full-dimensional convolution (ODConv*) method is proposed to improve the YOLOv5 detection algorithm for UAV complex ground object detection, and a detector Dy-YOLOv5 is designed for identifying complex ground targets. This detector is based on the YOLOv5 algorithm by adding a detection head specifically for UAV complex ground object detection, and The partial convolution in YOLOv5 is improved using a new dynamic full-dimensional convolution (ODConv*), while a regression loss function F-EIOU is introduced, and extensive simulation experiments are conducted on the ground UAV complex ground target dataset. The experiments show that the average accuracy of Dy-YOLOv5 is 19.7% higher than that of YOLOv5.
A Multi-scale Domain Adaptive Framework for Scene Text Detection

Scene text detection is a key and challenging research task in the area of computer vision, which has achieved superior performance with the development of deep learning. However, the model which had highly results relies on large-scale labeled datasets generally as well as large-scale manual labeling was laboriously and difficultly. Due to this, there is indeed a large difference between train and test data which may lead to a severe performance drop. To tackle this issue, we propose a domain adaptation model based on semantic segmentation network aiming at improving cross-domain robustness for scene text detection. Specifically, our model improving the performance of cross-domain problem on two components:(1) global-feature domain classifier module and (2) image-level domain classifier module, which can reduce the feature distributions and the scale difference of source domain and target domain effectively, and we add a consistency regularizer between the two modules to improve the generalization ability of the model. We set up two cross-domain scenarios to evaluate our proposed method. Empirical results demonstrate the effectiveness of our proposed model for domain adaptation scene text detection.
BeiDou Short Message Transmission Method Based on High-quality Image Compression and Reconstruction

Limited by bandwidth, Beidou short message system has certain limitations in the application of image transmission. To improve the problem, this paper proposes a Beidou short message transmission method based on high-quality image compression and reconstruction. We introduce the current mainstream deep learning framework based on variational autoencoders as our baseline. In order to achieve high-quality image compression and reconstruction, firstly, the attention mechanism is added to make the network automatically pay attention to beneficial information. Secondly, the image quality discriminator is designed that can provide the image definition score explicitly and is differentiable. Furthermore, based on the discriminator, a two-stage training method is proposed: first, the discriminator is trained with the data set synthesized by the Gaussian Blur, then the weights of the discriminator are fixed and the output of the discriminator is used to guide the network to generate images with high quality. The effectiveness of the method is verified by a series of experiments.
Marine debris detection model based on the improved YOLOv5

With the increasing global marine pollution, the detection and treatment of marine debris is particularly important. With the continuous development of machine learning, the object detection model has become a powerful tool for marine debris disposal. This paper proposes an improved YOLOvS model [1], [2], which changes backbone to mobileNet[3] on the basis of the original YOLOv5s model and introduces an attention mechanism to filter key features. The results show that on the TrashCan dataset[4], the detection precision and recall rate of our model have reached 79% and 63% respectively, and the detection effect has been improved by 9% and 2% compared with YOLOv5, respectively. Compared with the current underwater target detection model YOLOTrashCan[5], which has higher detection accuracy, the mAP@0.5 has been improved by 2.0%, realizing the accurate detection of marine debris in the real underwater environment. The research shows that our model has high precision while reducing model parameters and improving reasoning speed.
Research on Sentence Embeddings for Text Matching through Multiview Interactive Features

In this paper, we propose a sentence embedding for text matching method through multi-view interactive features: SBERT-Inter. This method is improved from the Sentence-BERT(SBERT) model based on semantic matching. Existing text matching models including representation-based methods get the vector representation of two sentences and calculate the similarity, but the interaction between sentences is lost. Or based on the interactive method to splice two sentences and directly output the similarity, but not through the sentence representation. Aiming at the problems of the two methods, a text matching method based on representation and interaction is proposed, which combines more fine-grained interaction modules after obtaining the representation features, through the multi-view and multi-perspective fusion of sentence and token features such as embedding difference, embeddings similarity, and token's self-attention, we can better learn the fine-grained interactive features between embeddings and we can get better semantic representation. It improves the matching performance between sentence pairs. On the public dataset semantic textual similarity benchmark(STS-B), the Spearman correlation of this method is enhanced by 6% compared with other sentence embedding methods.
MTL-JER: Meta-Transfer Learning for Low-Resource Joint Entity and Relation Extraction

Joint entity and relation extraction has achieved impressive advances in NLP, such as document understanding and knowledge graph construction. The typical methods for entity and relation extraction typically break down the joint task into several smaller components or stages for ease of implementation, but this leads to a loss of the interconnected knowledge in the triple. Hence, we propose to model the triple in one module jointly. Furthermore, the labeling of a joint entity and relation extraction tasks is costly and domain-specific; therefore, it is important to improve its performance on low-resource data and domain adaption. To address this issue, we suggest using two sources that are rich in information, namely pre-trained models on large data and multi-domain text corpora. Pretraining allows us to provide the model with the fundamental ability to perform joint entity and relationship extraction. Second, through meta-learning on multi-domain text, we can improve the model's generalization capabilities, enabling it to perform well even with limited data. We present MTL-JER, a Meta-Transfer Learning method for Joint Entity and Relation Extraction in low-resource settings in this paper. Using exhaustive experiments on five datasets, we prove that our model obtains optimal results.
Traffic light detection based on depth improved YOLOV5

Traffic light detection is an important part of intelligent driving, which affects the driving safety of intelligent vehicles. However, the traffic light is small and the actual situation is complex, and the classical object detection algorithm cannot achieve good detection effect on small traffic lights. In view of this situation, an improved yolov5 model is proposed in this paper. Inspired by u2net, a backbone network more suitable for signal light detection is proposed, and ConvNextBlock module is introduced to improve the feature extraction ability of the model. Through these improvements, the model is more sensitive to the target and the ability to recognize the object of signal light is improved. Experiments show that the improved algorithm achieves an excellent level of 81.5% in the performance index of Map _ 0.5 on the Bosch traffic light data set. In order to test the robustness of the model, the depth and width of the network are compared with the original yolov5 model in S, M and L, and the effect is improved.
Hierarchical Multi-label Text Classification Method Based On Multi-level Decoupling

In order to facilitate subsequent processing, the government hotline assigns hierarchical labels to the collected complaint and report texts. Classification of hierarchical multi-label text is a challenging task. Most previous studies regard hierarchical multi-label text classification as a flat multi-label classification problem, ignoring the constraints and connections between hierarchical labels. In this paper, we set hierarchical multi-label text classification as a sequence generation task, and propose a sequence-to-sequence-based hierarchical multi-label text classification model. Mainly use the method of multi-level decoupling to make better use of the connection between hierarchical tags, transform the constraints between tags into usable information, and help better classification. Compared with other models, the model proposed in this paper has a significant effect in the text classification of 12345 wading-related complaints and reports in Beijing.
Translation Validation of Information Leakage of Compiler Optimizations

The functional correctness of compiler optimization does not ensure the security properties of the source program. A functionally correct compiler optimization may introduce new security vulnerabilities in the optimized program. In a compiler, ensuring the security of every optimization is a challenging task as it applies hundreds of optimizations. Thus, this work aims to develop a translation validation of information leakage for the optimization phase of the compiler without considering any intermediate information from the compiler. Our method measures the information leakage in a path, uses a concept of leak propagation over paths and loops, and defines the relative security between the source and optimized programs. This work considers two attack models based on different observation points to access the local memory and proposes different translation validation approaches. The correctness and complexity of the translation validation method are also provided. The experimental results in the SPARK compiler on various benchmarks show that the optimized program is not relatively secure in many cases.