Reviewerly/exHarmony huggingface dataset:
ELFLN: An Efficient Lightweight Facial Landmark Network Based on Hybrid Knowledge Distillation

A facial landmark detector based on coordinate regression has minimal parameters and memory consumption, making it suitable for deployment on mobile devices. Typically, it employs a lightweight network as the backbone, but this network is not capable of effectively extracting features. Knowledge distillation is a promising methodology for developing a precise and lightweight network. The existing lightweight student networks do not have corresponding teacher networks, thereby hindering their ability to leverage the knowledge distillation technique. To tackle the inefficiency of the lightweight network in extracting features, we present an efficient lightweight network in this study, named ELFLN. In addition, we propose a novel hybrid knowledge distillation (HKD) framework to address the problem that the inadequacy of the lightweight network in carrying out features knowledge distillation. Finally, we augment our ELFLN by integrating facial landmark detection with head pose estimation, thereby enhancing the network generalization capability. To verify the efficacy of our proposed approach, we perform comprehensive experiments on 300W and WFLW datasets, achieving NME reach of 3.20% on 300W and 4.12% on WFLW with ELFLN+HKD. The number of parameters of ELFLN is observed to reduce by 62%, and FLOPs diminish by 87% compared to state-of-the-art SLPT.
Local-enhanced multi-scale aggregation swin transformer for semantic segmentation of high-resolution remote sensing images

Semantic segmentation of remote sensing images is crucial for various practical applications. In the field of deep learning, convolutional neural network (CNN) has been the primary approach for semantic segmentation over the past decade. Recently, Transformer-based models have achieved superior segmentation performance due to their exceptional global modelling capabilities. However, the Transformer-based models tend to focus more on extracting global contextual information, leading to suboptimal performance in segmenting local edges and difficulties in preserving fine-grained details during the patch token downsampling process. Inspired by the local receptive field of CNN, this article proposes a Local-Enhanced Multi-Scale Aggregation Swin Transformer (LMA-Swin) for semantic segmentation of high-resolution remote sensing images. Specifically, we adopt Swin Transformer as main encoder, introduce convolutional blocks as auxiliary encoder, and design a feature modulation module (FMM) to integrate the local contextual modelling ability of CNN into the Transformer backbone. Additionally, we propose a novel cross-aggregation decoder (CAD) to effectively aggregate shallow edge information and deep semantic information, thereby enhancing the discriminative ability for multi-scale objects. On the ISPRS Vaihingen and Potsdam datasets, experimental results illustrate noteworthy improvement in segmentation performance accomplished through the proposed approach. Code: https://github.com/patricklee16/LMA-Swin.
Economic, Socio-Cultural and Environmental Influences on Relapse of Drug Abuse at the Pelita Jiwa Insani Padang Foundation

One way to recover from drug abuse is by facilitating free rehabilitation. However, even though they have undergone rehabilitation to get rid of drug addiction, quite a few of them have a tendency to use drugs again. This research was conducted at the Pelita Jiwa Insani Foundation, Padang. This research was conducted in November 2023. This research uses a quantitative and qualitative approach with a descriptive design in the form of a case study. Data were collected using questionnaires and research checklists as well as observation guides. The research results showed that 100% of all informants stated that they had been using drugs for a long time. Their habit of using drugs stems from a process of curiosity and continuing to try drugs. Furthermore, the economic influence shows that of the 10 respondents studied, 6 respondents had high economic status and were working and experienced a relapse and 4 respondents had low status and did not work and experienced a relapse. The socio-cultural influence on the curiosity aspect of using drugs has a percentage of 70%. In the prestige aspect, the percentage is 20%. In the aspect of drug use to increase stamina, the percentage is 80%. Meanwhile, the aspect of eliminating boredom received a percentage score of 70%. Family, community and peer environment factors can cause relapse
Dynamic Visual Prompt Tuning for Parameter Efficient Transfer Learning

Parameter efficient transfer learning (PETL) is an emerging research spot that aims to adapt large-scale pre-trained models to downstream tasks. Recent advances have achieved great success in saving storage and computation costs. However, these methods do not take into account instance-specific visual clues for visual tasks. In this paper, we propose a Dynamic Visual Prompt Tuning framework (DVPT), which can generate a dynamic instance-wise token for each image. In this way, it can capture the unique visual feature of each image, which can be more suitable for downstream visual tasks. We designed a Meta-Net module that can generate learnable prompts based on each image, thereby capturing dynamic instance-wise visual features. Extensive experiments on a wide range of downstream recognition tasks show that DVPT achieves superior performance than other PETL methods. More importantly, DVPT even outperforms full fine-tuning on 17 out of 19 downstream tasks while maintaining high parameter efficiency. Our code will be released soon.
Learning-Without-Forgetting via Memory Index in Incremental Object Detection

Object detection has made significant progress in recent years. However, when the training data is continuous and dynamic, notorious catastrophic forgetting will occur. In addition, training together with old class data requires more storage space and training time, and even the old data is not available. To address the above challenges in incremental object detection (IOD), this paper proposes an one-stage and anchor-based IOD paradigm via parameter isolation with memory index (LwF-MI) that does not require any old data. The core of LwF-MI is to design the anchor-based classification masks for memory indexing to guide the incremental network output. Besides, we propose a multi-scale fusion method for the preprocessing of the above masks, which addresses the poor performance of large-scale masks. Extensive experiments on MS COCO demonstrate that our approach achieves state-of-the-art results, which even exceed the full training results.
MixPose: 3D Human Pose Estimation with Mixed Encoder

The fusion of spatio-temporal information is crucial for 3D human pose estimation in video. Existing methods usually extract temporal information from the spatially encoded poses, which may lead to limited spatio-temporal information interaction. To address this issue, we propose MixPose, a novel network for 3D human pose estimation with mixed encoder in videos. We introduce independent mixed encoders to fuse spatio-temporal information in the sequence, and augment the perception of each point with global information using an attention module. We evaluate MixPose on two public datasets, Human3.6M and HumanEva, experiment results show that MixPose outperforms other state-of-the-art methods in specific scenarios.
The Problem of Adaptation of Foreign Students to Online Learning Practices at Russian Universities

The article presents the results of an exploratory sociological study conducted in September-October 2023 on the problem of the influence of online learning educational practices on the social adaptation of foreign stu-dents at universities in Russia. The empirical object of research was Chinese citizens studying at Lomonosov Moscow State University and currently located on the territory of the Russian Federation. The study, using an online survey, obtained answers to questions about the attitude of Chinese students to the organization of the educational process, the practices of their interaction and communications in small study groups. It has been empirically proven that the attitude of Chinese students to any form of education depends on the extent to which the issue of overcoming the language barrier and building intercultural communication is resolved in a particular educational practice. At the same time, they associate a positive attitude towards online learning with the opportunity to study without the need to be on the territory of the Russian Federation, and a negative one with communication problems, particularly the lack of direct communication with a teacher.
Transforming Customer Experience in the Airline Industry: A Comprehensive Analysis of Twitter Sentiments Using Machine Learning and Association Rule Mining

The airline industry places significant emphasis on improving customer experience, and Twitter has emerged as a key platform for passengers to share their opinions. This research introduces a machine learning approach to analyze tweets and enhance customer experience. Features are extracted from tweets using both the Glove dictionary and n-gram methods for word embedding. The study explores various artificial neural network (ANN) architectures and Support Vector Machines (SVM) to create a classification model for categorizing tweets into positive and negative sentiments. Additionally, a Convolutional Neural Network (CNN) is developed for tweet classification, and its performance is compared with the most accurate model identified among SVM and multiple ANN architectures. The results indicate that the CNN model surpasses the SVM and ANN models. To provide further insights, association rule mining is applied to different tweet categories, revealing connections with sentiment categories. These findings offer valuable information to help airline industries refine and enhance their customer experience strategies.
[Design of flexible wearable sensing systems].

The aging population and the increasing prevalence of chronic diseases in the elderly have brought a significant economic burden to families and society. The non-invasive wearable sensing system can continuously and real-time monitor important physiological signs of the human body and evaluate health status. In addition, it can provide efficient and convenient information feedback, thereby reducing the health risks caused by chronic diseases in the elderly. A wearable system for detecting physiological and behavioral signals was developed in this study. We explored the design of flexible wearable sensing technology and its application in sensing systems. The wearable system included smart hats, smart clothes, smart gloves, and smart insoles, achieving long-term continuous monitoring of physiological and motion signals. The performance of the system was verified, and the new sensing system was compared with commercial equipment. The evaluation results demonstrated that the proposed system presented a comparable performance with the existing system. In summary, the proposed flexible sensor system provides an accurate, detachable, expandable, user-friendly and comfortable solution for physiological and motion signal monitoring. It is expected to be used in remote healthcare monitoring and provide personalized information monitoring, disease prediction, and diagnosis for doctors/patients.人口老龄化及老年人慢性病患病率的增加，给家庭和社会带来了巨大的经济负担。新型非侵入式可穿戴传感系统不仅可以连续实时地监测人们的重要生理体征，评估个人健康状况，并且可以提供高效、便捷的信息反馈，从而减轻老年人慢性病带来的健康风险。本文围绕可穿戴生理信号及运动信号监测，开发了一套针对人体生理信号和行为信号检测的可穿戴系统，探索了柔性可穿戴传感技术的设计以及在传感系统中的应用。系统包含了智能帽、智能衣、智能手套和智能鞋垫，可实现对人体生理信号和运动信号的长程连续监测。文章对系统进行了性能验证，同时将新型传感系统和商用设备进行比较。评估结果表明，新型系统的性能与现有系统相当。总之，新型柔性传感系统为生理和运动信号监测提供了一种精准、可拆卸、可扩展、用户体验友好和舒适的解决方案，有望用于远程医疗监测，为医生/患者提供个性化信息监测、疾病预测和诊断。.
LMFRNet: A Lightweight Convolutional Neural Network Model for Image Analysis

Convolutional neural networks (CNNs) have transformed the landscape of image analysis and are widely applied across various fields. With their widespread adoption in fields like medical diagnosis and autonomous driving, CNNs have demonstrated powerful capabilities. Despite their success, existing models face challenges in deploying and operating in resource-constrained environments, limiting their practicality in real-world scenarios. We introduce LMFRNet, a lightweight CNN model. Its innovation resides in a multi-feature block design, effectively reducing both model complexity and computational load. Achieving an exceptional accuracy of 94.6% on the CIFAR-10 dataset, this model showcases remarkable performance while demonstrating parsimonious resource utilization. We further validate the performance of the model on the CIFAR-100, MNIST, and Fashion-MNIST datasets, demonstrating its robustness and generalizability across diverse datasets. Furthermore, we conducted extensive experiments to investigate the influence of critical hyperparameters. These experiments provided valuable insights for effective model training.
The Use of Talk-King Book to Enhance Pupils Reading Comprehension Skill in Malaysian Primary ESL Classroom: A Concept Paper

An essential ability for children' academic performance and lifetime learning is reading. ESL primary learners face challenges in reading, especially when it comes to reading comprehension. Thus, this study proposes to examine how well the interactive Talk-King Book works to improve reading comprehension in Malaysian primary ESL classrooms. The theoretical framework for this study is based on the ideas of interactive learning and technology integration in the classroom. The Technical and Pedagogical Content Knowledge (TPACK) framework will be incorporated, in order to explain the interaction between technical, pedagogical, and content knowledge, which will govern the development and execution of the Talk-King Book intervention, which encourages reading comprehension. As such, this research will use quasi-experimental design. This design allows comparing two groups: an experimental group that receives the intervention (the use of Talk-King Book) and a control group that does not. The design enables the researchers to assess the impact of Talk-King Book on reading comprehension by measuring both groups' pre- and post-test scores to gather data.Descriptive and inferential statistics, such as t-tests or ANOVA will be used to analyse the data that are gathered. In this study, employing Talk-King Book is projected to greatly improve primary ESL learners' reading comprehension skills. The results of this study will add to the Body of Knowledge on interactive and technology integration on reading enhancement. Findings from this study will have implications for curriculum designers, educators, and legislators on the potential advantages of integrating interactive into reading teaching. The findings of this study will also have implications for curriculum designers, educators, and legislators on the potential advantages of integrating interactive into reading teaching. The study emphasises the significance of taking into account pupils' preferences and levels of involvement when creating and putting into practise technology-based interventions.
Research on Control Strategy of Two Arm Collaborative Robot Based on Vision

&lt;p&gt;Double arm robots are increasingly being used in automated production due to their higher work efficiency and better flexibility. This article focuses on the research of visual recognition based robots applied in automated production lines. Firstly, the visual system has been improved and image labeled to make it more accurate in identifying targets. Then, path planning has been carried out for the coordinated movement of both arms. Through the unconstrained collaborative planning method of both arms, the efficiency and robustness of path planning have been improved. Finally, experimental simulation has been conducted to verify the effectiveness of visual assistance conditions. The effectiveness of the dual arm planning method.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
ANALISIS KERENTANAN KEJAHATAN ONLINE PHISING MENGGUNAKAN TOOLS ZPHISHER, SHELLPHISH DAN WHPHISHER

In the era of increasingly advanced and widespread technology as it is today, personal data is an asset that really needs to be protected by an organization or individual, personal data information that is private is very important to be protected so that it cannot be accessed by unauthorized parties. Phishing attacks are currently still increasing, this happens because the perpetrators of phishing attacks understand to trick victims where the victim is still weak to understand the security system on personal data information. The purpose of this article is to explain how these phishing attacks can still be carried out, so that technology users can better understand phishing attacks and further improve the security of users' personal data themselves. Website phishing attack technique is one type of phishing attack that can be done with several types of tools including Zphisher, Shellphish and WhPhisher. With the exposure of this article, technology users get information regarding phishing website attacks and actions to deal with them. &#x0D; &#x0D; Keywords: website phishing, Zphisher, Shellphish, WhPhisher
Blockchain and IoT in the Modern Digital Age

In blockchain technology, there is no central authority controlling what happens, but there is a need for a consensus between more than half of the participants who check and validate the transaction. The IoT enables intelligent interconnection between these devices, everywhere through the internet. The use of IoT enhances the use of solutions based on Blockchain guarantee that certain information or transaction is really “true” to be recorded in the Blockchain, comes through verification, and in that sense IoT sensors can generate the evidence necessary that a certain fact happened, guaranteeing the veracity of each transaction registered. The blockchain's differential, makes these devices communicate without the need for a central unit to identify them, eliminating the need for this “central agent” to approve and validate transactions. Therefore, this chapter aims to provide an updated overview of Blockchain and IoT technologies, showing the fundamentals of this disruptive technology, demonstrating where integration between IoT and Blockchain is important, and featuring the potential of technologies.
Effectively Improving Data Diversity of Substitute Training for Data-Free Black-Box Attack

Recent substitute training methods have utilized the concept of Generative Adversarial Networks (GANs) to implement data-free black-box attacks. Specifically, in designing the generators, the substitute training methods use a similar structure to the generators in GANs. However, this design approach ignores the potential situation that the generators in GANs operate under real data supervision, while the generators in substitute training methods lack such supervision. This difference in data-supervised conditions constrain the diversity of data generated by the substitute training methods, resulting in inadequate data to support effective training of the substitute model. This impacts the substitute model's ability to attack the target model further. Consequently, to solve the above issues, we propose three strategies to improve the attack success rates. For the generator, we first propose a dense projection space that projects the input noise into various latent feature spaces to diversify feature information. Then, we introduce a novel disguised natural color mode. This mode improves information exchange between the generator's output layer and previous layers, allowing for more diverse generated data. Besides, we present a regularization method for the substitute model, called noise-based balanced learning, to prevent the potential risk of overfitting due to the lack of diversity of the generated data. In the experimental analysis, extensive experiments are conducted to validate the effectiveness of these proposed strategies.
Federated Learning for distribution skewed data using sample weights

One of the most challenging issues in federated learning is that the data is often not independent and identically distributed (non-IID). Clients are expected to contribute the same type of data and drawn from one global distribution. However, data are often collected in different ways from different resources. Thus, the data distributions among clients might be different from the underlying global distribution. This creates a weight divergence issue and reduces federated learning performance. This work focuses on improving federated learning performance for skewed data distribution across clients. The main idea is to adjust the client distribution closer to the global distribution using sample weights. Thus, the machine learning model converges faster with higher accuracy. We start from the fundamental concept of empirical risk minimization and theoretically derive a solution for adjusting the distribution skewness using sample weights. To determine sample weights, we implicitly exchange density information by leveraging a neural network-based density estimation model, MADE. The clients’ data distribution can then be adjusted without exposing their raw data. Our experiment results on three real-world datasets show that the proposed method not only improves federated learning accuracy but also significantly reduces communication costs compared to the other experimental methods.
Shape Sensitive Feature Extraction for Large Aspect Ratio Object Detection

The detection of objects with larger aspect ratios (OLAR) is a challenging problem in a special application scenario, such as remote sensing object recognition and scene text detection. However, current object detectors perform poorly in OLAR feature extraction because they are incapable of adaptively responding to object shapes, which leads to severe misalignment between impure feature representations and region proposals. In this letter, we aim at solving this problem by proposing our shape-sensitive convolution network (SSC-Net). SSC-Net is carefully embedded with a feature enhancement module (SSC module) specifically suitable for OLAR. This module can use fewer sampling points to achieve more intelligent feature sampling area transformation, thus achieving the goal of enhancing OLAR feature representation. Extensive experiments on benchmark datasets that are rich in OLARs have proved the superiority of our method. Besides, we further verified the plug-and-play performance of the SSC module, and the experimental results show that it can significantly improve the detection performance of the detector for OLAR.
SSRR: Structural Semantic Representation Reconstruction for Visible-Infrared Person Re-Identification

Visible- infrared Person Re-identification (VI-ReID) aims to retrieve the images of pedestrian with the same identity from different modalities and cameras given a pedestrian image. To reduce modality discrepancy, existing methods often perform hard partitioning to mine more detail. However, these methods employ only uniform partitioning, without considering pedestrian structure, and lose a lot of pedestrian semantic information. To this end, this paper proposes a structural semantic representation reconstruction (SSRR) method to capture pedestrian semantic information by focusing on pedestrian structure. Specifically, based on the fine-grained features obtained by hard partitioning, we carry out structural reconstruction to obtain the reconstructed features containing semantic information. By adopting the direct link reconstruction structure, the reciprocal learning of fine-grained features and semantic features is ensured. Semantic features are reconstructed based on fine-grained features, and semantic information is beneficial to fine-grained features to better capture pedestrian-related details. In addition, local consistency loss is introduced to ensure the consistency of fine-grained features in the same component location, further enhancing the discriminant of the learned reconstructed representation. Extensive experiments confirm the superiority of our method on two public datasets SYSU-MM01 and RegDB.