Reviewerly/exHarmony huggingface dataset:
Methods of Improving Japanese-Chinese Machine Translation System through Machine Learning and Human-Computer Interaction

With globalization, the exchange of people between different countries is becoming more frequent. Due to different languages, there are serious obstacles to personnel exchanges. To a large extent, they hinder the growth of industries such as economy, culture and tourism in each country. The emergence of Machine Translation (MT) has effectively improved the problem of language barriers, and greatly reduced the workload of translators in text translation. However, MT does not have the same flexible flexibility as human translation. It just translates the text word by word, which is often difficult to meet people's higher needs. This paper proposed to build a Japanese-Chinese MT system and integrate machine learning and Human-Computer Interaction (HCI) technology into the system. To further enhance the efficiency of the system, enhancement algorithms were also applied to the system to optimize the performance of the system. From the experimental results, in terms of BLEU (Bilingual Evaluation Understudy) index, the average BLEU index of the algorithm in this paper was 8.59, and that of the traditional algorithm was 6.55. In terms of translation precision, the average precision of the algorithm in this paper was 91.53%, while that of the traditional algorithm was 87.28%. In terms of translation readability, the average readability of the algorithm in this paper was 93.32%, while that of the traditional algorithm was 89.22%. By comparison, the average BLEU index of the algorithm in this paper has increased by 2.04; the average accuracy of translation increased by 4.25%; the average readability increased by 4.1%. From the above data, it was evident that the enhancement algorithm can optimize the performance of the Japanese-Chinese MT system well.
Mitigating the performance sacrifice in DP-satisfied federated settings through graph contrastive learning

Currently, graph learning models are indispensable tools to help researchers explore graph-structured data. In academia, using sufficient training data to optimize a graph model on a single device is a typical approach for training a capable graph learning model. Due to privacy concerns, however, it is infeasible to do so in real-world scenarios. Federated learning provides a practical means of addressing this limitation by introducing various privacy-preserving mechanisms, such as differential privacy (DP) on the graph edges. However, although DP in federated graph learning can ensure the security of sensitive information represented in graphs, it usually causes the performance of graph learning models to degrade. In this paper, we investigate how DP can be implemented on graph edges and observe a performance decrease in our experiments. In addition, we note that DP on graph edges introduces noise that perturbs graph proximity, which is one of the graph augmentations in graph contrastive learning. Inspired by this, we propose leveraging graph contrastive learning to alleviate the performance drop resulting from DP. Extensive experiments conducted with four representative graph models on five widely used benchmark datasets show that contrastive learning indeed alleviates the models' DP-induced performance drops.
Visible-infrared person re-identification using high utilization mismatch amending triplet loss

Visible-infrared person re-identification (VIPR) is a task of retrieving a specific pedestrian monitored by cameras in different spectra. A dilemma of VIPR is how to reasonably use intra-modal pairs. Fully discarding intra-modal pairs causes a low utilization of training data, while using intra-modal pairs brings a danger of distracting a VIPR model's concentration on handling cross-modal pairs, harming the cross-modal similarity metric learning. For that, a high utilization mismatch amending (HUMA) triplet loss function is proposed for VIPR. The key of HUMA is the multi-modal matching regularization (MMMR), which restricts variations of distance matrices calculated from cross- and intra-modal pairs to cohere cross- and intra-modal similarity metrics, allowing for a high utilization of training data and amending the adverse distractions of intra-modal pairs. In addition, to avoid the risk of harming feature discrimination caused by MMMR preferring coherence in similarity metrics, a novel separated loss function assignment (SLFA) strategy is designed to arrange MMMR well. Experimental results show that the proposed method is superior to state-of-the-art approaches.
Substation rotational object detection based on multi-scale feature fusion and refinement

In modern energy systems, substations are the core of electricity transmission and distribution. However, similar appearance and small size pose significant challenges for automatic identification of electrical devices. To address these issues, we collect and annotate the substation rotated device dataset (SRDD). Further, feature fusion and feature refinement network (F3RNet) are constructed based on the classic structure pattern of backbone-neck-head. Considering the similar appearance of electrical devices, the deconvolution fusion module (DFM) is designed to enhance the expression of feature information. The balanced feature pyramid (BFP) is embedded to aggregate the global feature. The feature refinement is constructed to adjust the original feature maps by considering the feature alignment between the anchors and devices. It can generate more accurate feature vectors. To address the problem of sample imbalance between electrical devices, the gradient harmonized mechanism (GHM) loss is utilized to adjust the weight of each sample. The ablation experiments are conducted on the SRDD dataset. F3RNet achieves the best detection performance compared with classical object detection networks. Also, it is verified that the features from global feature maps can effectively recognize the similar and small devices.
A multilayered bidirectional associative memory model for learning nonlinear tasks

A multilayered bidirectional associative memory neural network is proposed to account for learning nonlinear types of association. The model (denoted as the MF-BAM) is composed of two modules, the Multi-Feature extracting bidirectional associative memory (MF), which contains various unsupervised network layers, and a modified Bidirectional Associative Memory (BAM), which consists of a single supervised network layer. The MF generates successive feature patterns from the original inputs. These patterns change the relationship between the inputs and targets in a way that the BAM can learn. The model was tested on different nonlinear tasks, such as the N-bit, Double Moon and its variants, and the 3-class spiral task. Behaviors were reported through learning errors, decision zones, and recall performances. Results showed that it was possible to learn all tasks consistently. By manipulating the number of units per layer and the number of unsupervised network layers in the MF, it was possible to change the level of nonlinearity observed in the decision boundaries. Furthermore, results indicated that different behaviors were achieved from the same set of inputs by using the different generated patterns. These findings are significant as they showed how a BAM-inspired model could solve nonlinear tasks in a more cognitively plausible fashion.
Image blind motion deblurring method with longitudinal channel and wavelet dynamic convolution

Deep learning methods have achieved great success in image deblurring. However, these methods also exist two limitations, the detailed information loss caused by the image encoding, and the poor robustness caused by fixed weight parameters. To address the two issues, we propose an image deblurring method based on DeblurGAN, which is good at removing motion blurs. Firstly, two structures are specially designed, respectively. On one hand, the longitudinal channel network structure is proposed. It can directly fuse the original image features into each feature layer of the encoder, so as to retain more detailed information of the original image. On the other hand, the wavelet dynamic convolution module is applied to the feature extraction process. It can generate dynamic wavelet kernels according to different input images rather than fixed convolution parameters. In addition, we use the improved activation function and loss function to retrain the network. The quantitative experimental results show that the PSNR and SSIM metrics of our method can reach 29.9605 and 0.9177 on the GoPro test set, respectively. The qualitative experimental results indicate that our method can retain the original image details well, and achieve promising robustness.
Domain adaptive person search via GAN-based scene synthesis for cross-scene videos

Person search has recently been a challenging task in the computer vision domain, which aims to search specific pedestrians from real cameras.Nevertheless, most surveillance videos comprise only a handful of images of each pedestrian, which often feature identical backgrounds and clothing. Hence, it is difficult to learn more discriminative features for person search in real scenes. To tackle this challenge, we draw on Generative Adversarial Networks (GAN) to synthesize data from surveillance videos. GAN has thrived in computer vision problems because it produces high-quality images efficiently. We merely alter the popular Fast R-CNN model, which is capable of processing videos and yielding accurate detection outcomes. In order to appropriately relieve the pressure brought by the two-stage model, we design an Assisted-Identity Query Module (AIDQ) to provide positive images for the behind part. Besides, the proposed novel GAN-based Scene Synthesis model that can synthesize high-quality cross-id person images for person search tasks. In order to facilitate the feature learning of the GAN-based Scene Synthesis model, we adopt an online learning strategy that collaboratively learns the synthesized images and original images. Extensive experiments on two widely used person search benchmarks, CUHK-SYSU and PRW, have shown that our method has achieved great performance, and the extensive ablation study further justifies our GAN-synthetic data can effectively increase the variability of the datasets and be more realistic. The code is available athttps://github.com/crsm424/DA-GSS.
Nested star-shaped objects segmentation using diameter annotations

Most current deep learning based approaches for image segmentation require annotations of large datasets, which limits their application in clinical practice. We observe a mismatch between the voxelwise ground-truth that is required to optimize an objective at a voxel level and the commonly used, less time-consuming clinical annotations seeking to characterize the most important information about the patient (diameters, counts, etc.). In this study, we propose to bridge this gap for the case of multiple nested star-shaped objects (e.g., a blood vessel lumen and its outer wall) by optimizing a deep learning model based on diameter annotations. This is achieved by extracting in a differentiable manner the boundary points of the objects at training time, and by using this extraction during the backpropagation. We evaluate the proposed approach on segmentation of the carotid artery lumen and wall from multisequence MR images, thus reducing the annotation burden to only four annotated landmarks required to measure the diameters in the direction of the vessel's maximum narrowing. Our experiments show that training based on diameter annotations produces state-of-the-art weakly supervised segmentations and performs reasonably compared to full supervision. We made our code publicly available at https://gitlab.com/radiology/aim/carotid-artery-image-analysis/nested-star-shaped-objects.
Mapping hydrothermal alteration of the Pulang porphyry copper deposit, SW China, using ASTER and ZY1-02D satellite data

Alteration minerals are important prospecting indicators, and multispectral and hyperspectral remote sensing technologies are important prospecting tools. Multispectral remote sensing data such as ASTER have been widely used for extracting alteration mineral information. The dataset is easy to access, cost-effective to use, and rich in rock and ore spectrum information. Recently, the use of alteration mineral identification technology and hyperspectral satellite data for fine prospecting has become feasible in China, considering the development of hyperspectral remote sensing technology, especially the launch of the ZY1-02D satellite in China. Pulang porphyry copper deposit, Southwest China, is a largescale, explored deposit with unclear peripheral prospecting. Therefore, principal component analysis (PCA) and mixed tuned matched filtering (MTMF) methods were used in this study to extract the mineralized alteration minerals of the Pulang porphyry copper deposit area from the ASTER multispectral data and ZY1-02D hyperspectral data. Then, Al-OH and Fe-Mg-OH alteration information was obtained from the ASTER data using the PCA method. Meanwhile, sericite, chlorite, and epidote alteration information was extracted using the MTMF method and ASTER data. With the ZY1-02D data, the alterations of sericite, chlorite, and epidote were extracted by the PCA and MTMF methods. A field verification showed that the overall accuracy of the ASTER-based alteration extraction was up to 0.57, and the overall accuracy of the ZY1-02D-based alteration extraction was up to 0.88. And circled six favorable areas of copper deposit based on alteration mapping. Therefore, the ZY1-02D data can be regarded as a reliable and promising tool for delineating potential prospecting areas according to the mapping of hydrothermal alteration information, which can be explored further in the prospecting field in the future. The eastern and northern parts of the first mining area should be further explored for potential prospecting.
BeeFlow: Behavior tree-based Serverless workflow modeling and scheduling for resource-constrained edge clusters

Serverless computing has gained popularity in edge computing due to its flexible features, including the pay-per-use pricing model, auto-scaling capabilities, and multi-tenancy support. Complex Serverless-based applications typically rely on Serverless workflows (also known as Serverless function orchestration) to express task execution logic, and numerous application- and system-level optimization techniques have been developed for Serverless workflow scheduling. However, there has been limited exploration of optimizing Serverless workflow scheduling in edge computing systems, particularly in high-density, resource-constrained environments such as system-on-chip clusters and single-board-computer clusters. In this work, we discover that existing Serverless workflow scheduling techniques typically assume models with limited expressiveness and cause significant resource contention. To address these issues, we propose modeling Serverless workflows using behavior trees, a novel and fundamentally different approach from existing directed-acyclic-graph- and state machine-based models. Behavior tree-based modeling allows for easy analysis without compromising workflow expressiveness. We further present observations derived from the inherent tree structure of behavior trees for contention-free function collections and awareness of exact and empirical concurrent function invocations. Based on these observations, we introduce BeeFlow, a behavior tree-based Serverless workflow system tailored for resource-constrained edge clusters. Experimental results demonstrate that BeeFlow achieves up to 3.2× speedup in a high-density, resource-constrained edge testbed and 2.5× speedup in a high-profile cloud testbed, compared with the state-of-the-art. BeeFlow also demonstrates superior robustness in scenarios with heavy system workloads.
Multiple Target Activity Recognition by Combining YOLOv5 with LSTM Network

Human detection plays an important role in several fields (such as autonomous mobile robots, bio-medical applications, military applications, etc.) and has received considerable attention from researchers in recent years. Especially, human gesture recognition provides information to predict human behavior for collision avoidance of the robots. The present paper proposes an approach in which deep-learning method and machine-learning method are integrated to classify activities and movements for multiple human targets. The proposed recognition process involves three sequential steps, namely the YOLOv5 model for detecting targets, the Media Pipe for drawing the skeleton, and the LSTM network for recognizing activities. The proposed method is examined through different scenarios. In the case of detecting the target with YOLOv5, the experimental results show that the loss accuracy always maintains below 5% for both training and the validation processes, and the mean Average Precision (mAP) of the designed YOLOv5 model is always higher than 99% for all consideration case studies. Furthermore, the activity recognition performance of the proposed method also successfully detects and tracks the behavior of the defined target inspected via three case studies: sitting, standing, and hands up. The experimental results prove the stability and precision of the method and point out that this approach can be applied to further studies and applications.
L3Cube-MahaSBERT and HindSBERT: Sentence BERT Models and Benchmarking BERT Sentence Representations for Hindi and Marathi

Sentence representation from vanilla BERT models does not work well on sentence similarity tasks. Sentence-BERT models specifically trained on STS or NLI datasets are shown to provide state-of-the-art performance. However, building these models for low-resource languages is not straightforward due to the lack of these specialized datasets. This work focuses on two low-resource Indian languages, Hindi and Marathi. We train sentence-BERT models for these languages using synthetic NLI and STS datasets prepared using machine translation. We show that the strategy of NLI pre-training followed by STSb fine-tuning is effective in generating high-performance sentence-similarity models for Hindi and Marathi. The vanilla BERT models trained using this simple strategy outperform the multilingual LaBSE trained using a complex training strategy. These models are evaluated on downstream text classification and similarity tasks. We evaluate these models on real text classification datasets to show embeddings obtained from synthetic data training are generalizable to real datasets as well and thus represent an effective training strategy for low-resource languages. We also provide a comparative analysis of sentence embeddings from fast text models, multilingual BERT models (mBERT, IndicBERT, xlm-RoBERTa, MuRIL), multilingual sentence embedding models (LASER, LaBSE), and monolingual BERT models based on L3Cube-MahaBERT and HindBERT. We release L3Cube-MahaSBERT and HindSBERT, the state-of-the-art sentence-BERT models for Marathi and Hindi respectively. Our work also serves as a guide to building low-resource sentence embedding models.
AI-ML Analytics: A Comprehensive Investigation on Sentimental Analysis for Social Media Forensics Textual Data

Individuals spend a significant portion of their time on social media. It has become a platform for expression of feelings, sharing of ideas and connecting with other individuals using video and audio posts, textual data such as comments and descriptions and so on. Social media has a considerable impact on people’s daily life. In recent time, there is an enormous growth in number of people using Twitter and Instagram to share their emotions and sentiments which represents their actual feelings. In this work, we apply Machine Learning techniques on social media data to perform a comprehensive investigation to detect the risk of depression in people. Our work can help to detect various symptoms such sadness, loneliness, detachment etc. providing an insight for forensic analysts and law enforcement agencies about the person’s mental state. The experimental results show that Extra Tree Classifier performs significantly better over the other models in detecting the sentiment of people using social media data.
Analysis of Higher Education SIAKAD Website Security Gaps Using the Vulnerability Assessment Method

SIAKAD is a website-based application designed to facilitate academic management and academic activities by universities. The SIAKAD system contains sensitive and important information from lecturers and students such as personal, academic, and financial information. Seeing the importance of information security, universities must be able to ensure that their SIAKAD is protected by a strong and reliable security system. To find out threats that can be exploited by hackers, it is necessary to analyze security holes in a system. The research procedure was carried out through (1) Terminal, Zenmap, Whois, Wappalyzer as tools for foot printing or data collection, (2) OWASP ZAP as a tool for conducting Vulnerability Scanning. The results of this study revealed that there were 11 vulnerabilities found, consisting of 5 medium vulnerability levels and 6 low level vulnerabilities. In addition, to deal with these vulnerabilities, this research also discusses solutions that can be alternatives for increasing information security in higher education SIAKAD.
SFTN: Fast object detection for aerial images

Abstract The task of remote sensing image object detection in low latency scenes is of great research significance. To address the problem that the current high‐precision object detection algorithm based on a feature pyramid network is slow due to a large number of parameters and complicated computation, a fast remote sensing image object detection method based on a Single‐scale Feature Transformation Network (SFTN) is proposed. Firstly, based on the single‐scale remote sensing image features, the new channel features are quickly generated by a linear transformation of the original features and convolution kernel clustering optimization using cosine similarity; secondly, in order to obtain multi‐scale receptive fields, a parallel residual hole convolution module is designed to cover multi‐category remote sensing object scales on the feature map; finally, angle variables are introduced and optimized using angle similarity to effectively improve the object orientation accuracy. The experimental results on different datasets show that the method in this paper improves the detection speed rapidly while ensuring the accuracy of remote sensing image object detection, which is better than many remote sensing image object detection methods. The results demonstrate the reliability and robustness of the method.
High-performance fractional anisotropic diffusion filter for portable applications

Abstract Anisotropic diffusion is one of the most effective methods used in image processing. It can be used to eliminate the small textures of an image while preserving its significant edges. In this paper, a new anisotropic diffusion filter is proposed based on a fractional calculus kernel rather than integer kernel to improve the overall performance of the filter. Integer and fractional anisotropic filters are implemented using the Genesys-2 FPGA kit to utilize the efficiency of parallelism in FPGAs. Integer and fractional anisotropic filters are tested against the achievable PSNR value vs the number of iterations. The proposed fractional anisotropic filter has a better PSNR value using a smaller number of iterations, reducing the power and area compared to integer anisotropic filter. The proposed filter can be used in image smoothing, edge detection, image segmentation, image denoising, and cartooning. In addition, the proposed filter reduces the power consumption by 58.2% compared to integer-order filters, which makes the proposed filter suitable for battery-based applications.