Reviewerly/exHarmony huggingface dataset:
Double Distillation of BERT Combined with GCN for Text Classification

Currently, the BERT model in the field of natural language processing has better performance for natural language processing tasks compared to other models. However, the BERT model is limited by the resources of external devices when processing large-scale data sets. In this paper, we propose a combined GCN and BERT which using knowledge distillation technique to optimize the difficulties of BERT model to handle large-scale data sets limited by resources. The approach combines the advantages of BERT models and graph convolutional neural networks, incorporating the advantages of large-scale data training and conductive learning to perform natural language processing tasks. The model in this paper compresses the BERT model in the pre-training phase and then fuses it with GCN. The model reduces the model complexity in the pre-training phase and optimizes the problem of resource constraints when dealing with large-scale data. In this paper, four Public benchmark datasets are used to test the model. The model accuracy is higher than the original baseline model.
Improved Video QoE in Wireless Networks Using Deep Reinforcement Learning

Millions of videos are watched per minute on the Internet. Due to real-time performance demands, such as high-quality video streaming, network administrators face new challenges to control the network and cope with the expected quality of experience (QoE). Automatic control is a necessity to reduce the OPEX, because it could reduce the need for resource overprovisioning, as well as the number of human administrators. Dynamic rate in video streaming alleviates the resource usage, but it worsens the video quality when a network bottleneck occurs, lowering the QoE. This paper dynamically adjusts the IEEE 802.11 parameters to improve the network condition and hence maintain a higher QoE. While traditional networks are not aware of the application, in our proposal the controller learns the configuration of the access points (APs) (in terms of transmission power and channel number) that provide the best QoE, using double deep Q-Learning (DDQL). The proposal improves video QoE by 91 % in the best case, when compared to three baselines. It also balances the QoE among clients, improving the fairness up to 115% when compared to the baselines.
Federated Reinforcement Learning for Sharing Experiences Between Multiple Workers

Reinforcement learning has been successfully applied in various fields, such as games and robots. However, there are still some issues in the traditional reinforcement learning paradigm that involves one agent per environment, such low efficiency in data generation and insufficient amount of training data. In this paper, we propose a federated reinforcement learning framework that utilizes multiple agents to generate data for simultaneous training of models. Specifically, the proposed framework allows the best performing agent to share its learning experience with other agents for improving the learning performance and protecting the privacy of agents. The proposed framework is applied to the Cart-pole, Acrobot and LunarLander environments in OpenAI Gym, and the results show that a significant improvement of the learning performance can be achieved by adopting our framework.
Object Recognition and Spatial Relationship Determination in Point Clouds

In smart living, scene interpretation is necessary. Determining the spatial relationships of each object helps the system understand people's needs. For monocular machine vision of 2D images, it is difficult to obtain accurate spatial relationships at some angles. The point cloud obtained by the depth camera has a natural advantage for such objects. However, the object recognition of 3D point cloud is computationally intensive, and the original image recognition accumulated data set is completely abandoned. This paper combines the advantages of image recognition and point cloud, uses the existing model of image recognition to identify objects, and accurately finds the point cloud of the object in the point cloud, and then determines the spatial relationship. This paper first uses the 2D object recognition algorithm to find out the approximate range of each object and then uses the GNG clustering algorithm to remove the background point cloud and identify the corresponding 3D object. This method can effectively improve the accuracy and efficiency of 3D object recognition. Combining the GroundingDINO and SAM methods, it is possible to recognize and segment the point clouds of various objects in 3D by simply inputting their category names. By finding the centroid of each point cloud cluster after recognition, the accurate spatial relationship between objects can be obtained.
Blockchain-Based Self-Sovereign Identity for Federated Learning in Vehicular Networks

Self-Sovereign Identity (SSI) has emerged lately as an identity and access management framework that is based on Distributed Ledger Technology (DLT) and allows users to control their own data. Federate Learning (FL), on the other hand, provides a framework to update Machine Learning (ML) models without relying on explicit data exchange between the users. This paper investigates identity management and authentication for vehicle users, which are participating into FL. We propose a new approach to SSI, that is alternative to the conventional blockchain-based SSI, specifically for use in vehicular networks, which focuses on maintaining confidentiality, authenticity, and integrity of vehicle users' identities and data exchanged between the users and the aggregation server during the execution of the FL process. We also provide experimental results for distributed identity management (DIM) operations, which show that the performance of credential operations in the implemented system is generally efficient and the average times are within reasonable limits. However, there is a slight increase in presentation time, offer time, connection establishment time, and credential revocation time as the number of requests increases, indicating a slight degradation in performance for these operations.
Binary Decomposition for Multi-Class Classification Problems: Development and Applications

Binary decomposition of a multi-class classification problem is a widely used method in the field of machine learning, which involves using an ensemble of binary classifiers to undertake multi-class classification tasks. The motivation of binary decomposition lies in two main aspects. Firstly, some learning algorithms such as support vector machine can not achieve directly learning a multi-class classifier. Secondly, in some cases, the adoption of binary decomposition for training an ensemble of binary classifiers can even achieve better performance than the direct learning of a multi-class classifier. There are some existing methods that can be applied in achieving binary decomposition and have been proven to be effective. Some of these methods may also be applied in other tasks beyond classification. This paper presents a comprehensive review of the development and applications of binary decomposition methods. Specifically, these methods are put into two categories, namely, ordered and unordered decomposition, which each is introduced in terms of the essence and strategies of decomposition. These methods are also compared through theoretical analysis of their effectiveness and efficiency. On the basis of the comparative analysis, we suggest some future directions towards achieving further advances in binary decomposition.
Research on Security Data of Federated Learning Privacy LSTM Model Based on Generative Adversarial Network

As a new wireless transmission architecture technology, cSO can effectively reduce the influence of fading channels and provide cSO links for low channel quality. Secondly, in the ultra-large traffic cSO, The fitting of data and the constraint on the model must be considered at the same time. Generally speaking, regularization is the most common method, because in the process of inversion, it is far from enough to only use data fitting terms as constraints. In order to make the inversion results stable, model constraint terms should be added to the objective function on the basis of data fitting terms, and there are usually different model constraint methods for different practical problems. When the physical properties of anomalous body are different from those of surrounding rock, the minimum model constraint can be used. When inversion of the moderocess of EMM-connected state was consistent with the determination of the coverage enhancement level.
Lifting the Answer: Reranking Candidates on Data Augmented Text-to-SQL

The text-to-SQL task is a realistic and challenging job, which aims to translate natural language questions into corresponding SQL queries. When attempting to generate SQL queries in the text-to-SQL task, prevailing semantic parsing models employ beam search to generate several candidates. Based on our pilot study, we observe that the gold-standard SQL answer may exist in the n-best candidate list produced by the decoder, rather than the first. Hence, we aim to lift the correct answer given several candidates generated by semantic parsing models for text-to-SQL. To this end, we propose a reranking module that reorders the n-best list of candidate SQL queries by pair-wise hinge loss. Meanwhile, a data augmentation module is leveraged to enrich the inadequate training instances for providing candidates with better qualities. These two modules can be easily grafted onto text-to-SQL backbone networks, and extensive experiments on the cross-domain text-to-SQL benchmark Spider demonstrate that our method achieves 73.8% in accuracy on the Spider dataset, surpassing the base model by up to 3.5%.
Recent Advancements and Challenges in Multimodal Sentiment Analysis: A Survey

Sentiment analysis aims to extract people's underlying attitudes, opinions and thoughts toward various topics, products and services. Sentiment analysis is a popularly adopted technique that involves applications across domains such as business, healthcare, government and marketing. Traditional sentiment analysis methods mainly focus on text contents. However, recent technological advancements have enabled the expression of opinions through different forms of media, such as images or audios, leading to sentiment analysis using multiple modalities rather than text alone. Multimodal sentiment analysis results in a more comprehensive understanding of the emotions and opinions expressed in a given content by combining information from different modalities. A model of multimodal sentiment analysis involves three main parts, namely, unimodal feature extraction, multimodal fusion and sentiment polarity prediction. This paper provides a comprehensive overview of multimodal fusion methods along with their corresponding model architectures. Moreover, several more recently released models are introduced and the strengths and limitations of these models are discussed comparatively. Finally, some challenges and future research directions are identified.
Intelligent Control and the Transformation of Work Values of Skilled Workers

In this study, the relationship between intelligent control and the work values of skilled workers is studied. On the basis of dividing the skilled workers' work's value into three types, a description analysis is done to show the work values' present situation. Then, the impact models of intelligent control on skilled workers' judgment of work's value for individual, society, people's quality, and on skilled workers' judgment of work's priority in the future are discussed. Based on the training based learning, weight calculation model with BP Algorithm of feedforward neural networks was constructed. The study found that: (1) Intelligent control has a positive impact on the recognition of the work value for individual and society of skilled workers; (2) Intelligent control plays a positive role in skilled workers' assessing on the work's importance to human quality, and in assessing the value of work to children's quality; But it has a certain dispelling effect on skilled workers' judgment of the quality of labor for adults; (3)Intelligent control can improve the judgment of labor priority for skilled workers. Obviously, it is necessary to optimize the guidance and education strategies for the work values of skilled workers in the context of intelligent control.
A Self-Contained IoT Sensor Module for Realizing Drastic Cost Reduction

This paper proposed a self-contained IoT sensor module which includes sensor for collecting various information, micro-computer and multiple solar panels for realizing dynamic cost reduction such as CAPEX and OPEX. Proposed module has the feature of automatically grasping the installation state such as the inclination and direction of the module using the time change of energy generated by solar panels and numerical analysis on the cloud. Furthermore, it is possible to supply power to drive the sensors using generated solar energy.
Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks

Neural network pruning has shown to be an effective technique for reducing the network size, trading desirable properties like generalization and robustness to adversarial attacks for higher sparsity. Recent work has claimed that adversarial pruning methods can produce sparse networks while also preserving robustness to adversarial examples. In this work, we first re-evaluate three state-of-the-art adversarial pruning methods, showing that their robustness was indeed overestimated. We then compare pruned and dense versions of the same models, discovering that samples on thin ice, i.e., closer to the unpruned model's decision boundary, are typically misclassified after pruning. We conclude by discussing how this intuition may lead to designing more effective adversarial pruning methods in future work.
Detecting Attacks Against Deep Reinforcement Learning for Autonomous Driving

With the advent of deep reinforcement learning, we witness the spread of novel autonomous driving agents that learn how to drive safely among humans. However, skilled attackers might steer the decision-making process of these agents through minimal perturbations applied to the readings of their hardware sensors. These force the behavior of the victim agent to change unexpectedly, increasing the likelihood of crashes by inhibiting its braking capability or coercing it into constantly changing lanes. To counter these phenomena, we propose a detector that can be mounted on autonomous driving cars to spot the presence of ongoing attacks. The detector first profiles the agent's behavior without attacks by looking at the representation learned during training. Once deployed, the detector discards all the decisions that deviate from the regular driving pattern. We empirically highlight the detection capabilities of our work by testing it against unseen attacks deployed with increasing strength.
Adaptive Model Aggregation for Decentralized Federated Learning in Vehicular Networks

Decentralized federated learning (DFL) enables collaborative training of machine learning models without sharing sensitive data. As such, its application in vehicular networks has gained significant attention. At the core of DFL is the so-called model aggregation, in which each vehicle combines its locally trained model with those received from its neighboring vehicles to generate an updated model that, over time, converges to a global model shared by all vehicles. However, due to high mobility and wireless communication, vehicle-to-vehicle communication is lossy. As a result, a vehicle may receive its neighboring vehicle models only partially. A technical challenge in DFL is how to efficiently utilize such partial models to speed up model training without increasing the training overhead. In this paper, we present an Adaptive Model Aggregation (AMA) algorithm to address this challenge. Our algorithm runs asynchronously on each vehicle and uses a threshold to decide whether to use a partially received model for aggregation. We show that due to the mobility of vehicles, a static threshold is not sufficient and subsequently develop an algorithm based on the contextual multi-arm bandit theory to adaptively compute an optimal threshold for each vehicle based on the dynamics of the network. We evaluate the performance of AMA in realistic environments that include different mobility patterns. Our results show that AMA can decrease DFL aggregation overhead by 83% without reducing the training accuracy compared to non-adaptive aggregation.
Subscription Management for Beyond 5G and 6G Cellular Networks Using Blockchain Technology

As Mobile Network Operators (MNOs) prepare to interconnect diverse technologies to existing cellular networks, it is critical to assess the capabilities of the network architecture to handle such changes. One key area to consider is the subscription management process, which governs the user's profile management, authentication, and access control. This process operates centrally, which affects users' security, accessibility, and privacy. Additionally, it increases the system's complexity when handling the large volume of messages sent over networks like IoT. In this work, we propose a Blockchain-based subscription management approach for next generation cellular networks to address the challenges in user profile management and the Authentication and Key Agreement (AKA) process. The method uses a hybrid cryptosystem technique to protect the user's privacy. Based on the evaluation, the system can handle the AKA process with fewer messages passing while improving system availability by utilizing distributed network functions and storage. Finally, we highlight some key points to consider when implementing our proposed approach.
Visual Monitoring of Many Objects in Real Time Using Embedded GPU

In modern times, object tracking and detection play a crucial role in many real-life applications like the traffic sector, UAVs, and many other real-life applications. Generally, object detection and tracking have different approaches but we discuss a new approach in this article. Usually, object tracking and detection is a hard task it generally performs using AI and deep learning-based platforms so for that we require a heavy performance high-end hardware but previously we perform this task on low-power edge IoT devices but there are some drawbacks like limited computing and also compromising inaccuracy. So to improve the efficiency, accuracy, and performance we go for an embedded GPU platform which consists of a deep learning architecture and also the joint architecture of tracking and detection. By this combination system, the accuracy is increased on the MOT16 data set. And also the efficiency is increased to track the 45 simultaneous objects at a time with 25 fps.
Learning Speaking Using Guided Conversation Method to Increase Student Ability to Compose a Simple Dialoge

This article explores the use of innovative strategies in improving learners' ability to create simple dialog among Grade 8 learners at SMP Muhammadiyah 2 Taman Sidoarjo. The main focus of this research is to apply project-based learning model combined with guided conversation technique in learning the material of asking and giving opinion. Project-based learning as a learning model aims to stimulate the activeness, creativity of students in completing the learning. Learners are guided to create text in the form of a simple dialog with the theme of asking and giving opinion and then practiced with peers. By using guided conversation techniques, learners are guided intently to achieve learning objectives. Learning begins with the use of ice breaking relevant to the material, triggering questions, video footage, imitating conversations based on the text that has been provided until the practice of making conversations is a form of guidance to students. The research method applied in this study carries a combinative approach between project-based learning models with guided conversation techniques. The observation results showed a significant increase in students' active participation during the learning process. Students were more actively involved in group discussions, questions, and assignments. They are not only the recipients of information, but also the main actors in their own learning. This increased participation indicates that the models and techniques used are able to create a learning environment that stimulates more intensive student engagement. The conclusion we can draw is that this innovative strategy proves that the use of project-based learning model with guided conversation technique can be an effective step in improving learners' ability in the creation of speaking text in English language learning.
Adaptive Selection of BERT Layer for Multi-Label Text Classification

Multi-label text classification is a special type of natural language processing tasks, which is more complex than traditional single-label classification. Moreover, due to the key strength of pre-trained language models such as BERT in feature embedding at both token and sentence levels, transfer learning driven text classification has become increasingly popular. In this context, it is a commonly used strategy to take the output of the last layer of BERT as sentence embedding for text classification. However, the above-mentioned way of obtaining sentence embedding may not always be suitable for multi-label text classification, i.e., the best layer that leads to the optimal performance of classification may be varied for different labels, especially when a multi-label classification task is transformed into several binary ones. In order to address the aforementioned issue, we set a validation data driven way in this paper to explore the performance of sentence embedding obtained from each layer and adaptively select the best layer to output the sentence vector for each specific label. The experimental results show that using a sentence vector obtained from an intermediate layer may lead to better performance in comparison with simply taking the output of the last layer as the adopted sentence embedding. The results also indicate the effectiveness of the above-mentioned adaptive selection of the best layer in improving the performance of multi-label text classification.
Applying Machine Learning on Breast Mass Digitized Images to Predict Breast Cancer

Breast cancer develops in the glandular tissue of the breast affecting the cells that line the lobules. Early and accurate detection and receiving cancer treatment at earlier stages can prevent many deaths among millions of women across the world. The histological evaluation of a tumor generates a tremendous amount of data related to malignancy, but findings may vary depending on subjective analyses. Therefore, a computer-based diagnostic system is required for accurate diagnosis and early detection. This study analyzes breast cancer diagnostic dataset from the Wisconsin Breast Cancer Database using the IBM Watson Machine Learning Platform. The dataset contains 569 digitized images of individual breast masses (357 benign, 212 malign) with cell nuclei information. The dataset captured a range of properties, including radius, texture, and perimeter, that were employed as variables influencing the outcome. IBM Watson machine has the capability of developing models by using multiple algorithms. In this study, two algorithms with various enhancements were used including Snap Logistic Regression and Snap SVM Classifier with enhancements. Both algorithms reached an accuracy of about 97%. Some essential image features including the product of various factors like area-worst, concave points, symmetry worst, smoothness mean, and perimeter worst were found playing a significant role in predicting the cancer cells malignancy.