Reviewerly/exHarmony huggingface dataset:
Dual-Space Transfer Learning Based on an Indirect Mutual Promotion Strategy

Abstract Transfer learning is designed to leverage knowledge in the source domain with labels to help build classification models in the target domain where labels are scarce or even unavailable. Previous studies have shown that high-level concepts extracted from original features are more suitable for cross-domain classification tasks, so many transfer learning methods transfer knowledge by modeling high-level concepts on the original feature space. However, there are two limitations to this method: First, learning high-level concepts directly on the original feature space will reduce the proportion of shared information contained in common features in the process of knowledge transfer bridge construction. Second, only learning multiple high-level concepts on the original feature space, the latent shared information contained in the domain-specific features cannot be targeted learned, so the latent shared information in the domain-specific features cannot be effectively used. To overcome these limitations, this paper proposes a novel method named Dual-Space Transfer Learning based on an Indirect Mutual Promotion Strategy (DSTL). The DSTL method is formalized as an optimization problem based on non-negative matrix tri-factorization. DSTL first extracts the common features between domains and constructs the common feature space. Then, the learning of the high-level concepts of the common feature space and the original feature space is integrated through an indirect promotion strategy, which can enhance the learning effect of common features and domain-specific features through the mutual help of the two feature spaces. The system test on benchmark data sets shows the superiority of the DSTL method.
Similarity Retrieval Based on Image Background Analysis

Aiming at the problem of traditional portrait background similarity retrieval methods being low accuracy and time-consuming, a similarity retrieval method based on image background analysis is presented. The proposed method uses a combination of portrait segmentation and retrieval models. Firstly, the portrait segmentation model is used to remove the portraits in the images to eliminate the interference of portraits on background features; secondly, the image retrieval model is used to retrieve images with similar background features; LSH is added to improve the retrieval efficiency; finally, the retrieval results are used to further determine whether the background is similar. The experiment is implemented based on real data from a company. The results showed that the average precision, average map, and recall of this method reached 85%, 90%, and 50%, respectively. The average accuracy and recall are 10% better than the overall image retrieval model.
An extension to the Frenet-Serret and Bishop invariant extended Kalman filters for tracking accelerating targets

This paper presents an extension to the original Frenet-Serret and Bishop frame target models used in the invariant extended Kalman filter (IEKF) to account for tangential accelerations for highly-manoeuvrable targets. State error propagation matrices are derived for both IEKFs and used to build the accelerating Frenet-Serret (FSa-LIEKF) and Bishop (Ba-LIEKF) algorithms. The filters are compared to the original Frenet-Serret and Bishop algorithms in a tracking scenario featuring a target performing a series of complex manoeuvres. The accelerating forms of the LIEKF are shown to improve velocity estimation during non-constant velocity trajectory segments at the expense of increased noise during simpler manoeuvres.
Security Analysis of Social Network Topic Mining Using Big Data and Optimized Deep Convolutional Neural Network

This research aims to conduct topic mining and data analysis of social network security using social network big data. At present, the main problem is that users' behavior on social networks may reveal their private data. The main contribution lies in the establishment of a network security topic detection model combining Convolutional Neural Network (CNN) and social network big data technology. Deep Convolution Neural Network (DCNN) is utilized to complete the analysis and search of social network security issues. The Long Short-Term Memory (LSTM) algorithm is used for the extraction of Weibo topic information in the memory wisdom. Experimental results show that the recognition accuracy of the constructed model can reach 96.17% after 120 iterations, which is at least 5.4% higher than other models. Additionally, the accuracy, recall, and F1 value of the intrusion detection model are 88.57%, 75.22%, and 72.05%, respectively. Compared with other algorithms, the model's accuracy, recall, and F1 value are at least 3.1% higher than other models. In addition, the training time and testing time of the improved DCNN network security detection model are stabilized at 65.86 s and 27.90 s, respectively. The prediction time of the improved DCNN network security detection model is significantly shortened compared with that of the models proposed by other scholars. The experimental conclusion is that the improved DCNN has the characteristics of lower delay under deep learning. The model shows good performance for network data security transmission.
Cloud Computing Based on Big Data-Driven Robot Walking Route and Real-Time Positioning Intelligent Determination

With the continuous development of technologies such as sensors, computers, and artificial intelligence, intelligent mobile robots with thinking, perception, and dynamics functions are widely used in military, political, and scientific research. Its development has had a significant impact on national defense, society, economy, science, and technology and has become a strategic research goal in the high-tech field of various countries. Robot positioning technology is one of the key research technologies for portable robots, and reliable posture is the key prerequisite for completing various tasks. This article aims to study the robot walking route driven by big data and the intelligent determination of real-time positioning based on cloud computing. This paper proposes an active general positioning algorithm based on real-time positioning function, which can improve the convergence speed and robustness of general positioning when different map scenes do not have clear geometric features and contain map noise. The most basic requirement for robots to perform autonomous operations is to have reliable positioning performance. The experimental results in this paper show that dynamic global positioning and adaptive behavior tracking are effective. Compared with the traditional algorithm, the improved algorithm increases the convergence speed of the global layout by 41.59%.
Introduction—blockchain and smart grid

Blockchain technology is most simply defined as a decentralized, distributed ledger that records the provenance of a digital asset. Blockchain, sometimes referred to as Distributed Ledger Technology, makes the history of any digital asset unalterable and transparent through the use of decentralization and cryptographic hashing. A simple analogy for understanding blockchain technology is a Google Doc. When we create a document and share it with a group of people, the document is distributed instead of copied or transferred. This creates a decentralized distribution chain that gives everyone access to the document at the same time; doesn't lock out anyone awaiting changes from another party; while recording all modifications to the doc in real-time, making changes completely transparent. Smart grids are currently advancing technologically at a very fast pace by using borrowed capitals for the benefits offered by Wireless Sensor Networks and the Internet of Things. They not only offer optimization in energy production and energy consumption by the adoption of intelligent systems that can monitor and communicate with each other but also reduces manpower requirements with attendant enhanced accuracy that come with automation of the smart sensor-based metering system using Advanced Metering Devices. Thus, the grid is made more cost-efficient with enhanced performance and better intelligence achieving efficient energy utilization. Smart grids also promise more cost-efficient tapping of renewable sources of energy by offering technological support for the transfer of energy between local energy producers and consumers. The consumers who can harvest renewable sources of energy such as sunlight using rooftop solar panels can become producers-cum-consumers by selling their surplus energy either to neighboring consumers or to the grid. This promotes the urgency in consumers to utilize renewable sources of energy. Applications of blockchain in smart grid are Security and Privacy-Preserving Techniques, Energy Trading in Electric Vehicles, Peer-To-Peer Trading Infrastructure, and so forth. Blockchain is an especially promising and revolutionary technology because it helps reduce risk, stamps out fraud, and brings transparency in a scalable way for myriad uses.
Framework for Remote Device Localization and Application Level Visualization for Emergency Service Providers

During a crisis event, remote monitoring of the floor plan, devices, and crowd in an indoor public space is essential. To tackle such a situation it is imminent for the emergency service providers or the organization to gain real-time information of the floor map and the position(s) of the crowd affected by the crisis. In this paper, we intend to provide a framework for supporting smart emergency management for indoor space that constitutes of two parts- (i) remotely localizing the crowd on a near real-time basis and (ii) disseminating this information in a navigation-friendly manner to the emergency responders. A machine learning-based model trained on gathered WiFi Received Signal Strength(RSS) forms the basis of the positioning mechanism opted for our approach. A 3D visualization on a Virtual Reality(VR) platform is amalgamated at the application level to map the actual physical location(s) of the crowd to the virtual coordinate that will be displayed to the emergency responders.
Blockchain in monitoring and automation of distribution systems

The current development of power systems requires not only a transition toward sustainable generation technologies but also—at the same time—an increase in the overall reliability in an environment of distributed automation and Local Energy Communities. After a brief introduction to the topic, the first section of the chapter studies the characteristics of monitoring, control, and forecasting infrastructure necessary for the adoption of blockchain solutions. In the second section, we analyze current problems, propose solutions to overcome the lack of data, and review different blockchain-associated services for the creation of and information exchange on monitoring services. Then, different automation techniques such as Consensus Group creation over blockchain are covered; and later with a special emphasis on "Blockchain-based Virtual Redundancy," an automation solution based on BRILLIANT's architecture, the chapter presents the system aimed to overcome failures in critical control and monitoring infrastructure. Finally, a short discussion about the future of blockchain-friendly smart meters opens doors to making choices that have mutually exclusive outcomes: choosing resource-efficient processes like green, renewable technologies vis-à-vis resource rationalization as the choice between creating positive environmental impact (while protecting the privacy of consumers' data) and economic retrogression.
Blockchain in internet of entities - issues and challenges

After the invention of the internet of things (IoT), information sharing started happening through the connected cloud-based ecosystem. Blockchain had an important role to play in the expansion of the internet of entities (IoE) market as non-centralized networks were being deployed and used. IoE has emerged as one of the popular add-ons to IoT technology which helps in easy data collection, organizing, and managing of data in non-centralized databases. Gradually, many business organizations and technological institutions have started to adopt the Blockchain from data security and data privacy concerns. However, the adoption of Blockchain with IoE includes the challenges of scalability, energy consumption, interoperability, etc. Apart from these issues, other major hitches in the path of mass adoption of Blockchain technology are lack of regulations, lack of standardization, and shortage of talents. In this chapter, all the major issues and challenges of Blockchain in IoE are analyzed in detail.
New Trends in HCI and Sports

Over the last 15 years, we have witnessed a digitalization of the sports experience, i.e., many sports have been enhanced by digital and wearable devices. The centrality of the human body and the different contexts where sports can be practiced have led HCI research to explore how mobile and wearable devices could support the physical, social, and environmental aspects of sports disciplines. Yet, the field of HCI & sports continues to evolve under the push of new technological developments and events affecting people worldwide, such as the Covid-19 pandemic and climate change. Technological advancements like the metaverse, inbodied technologies, and AI have paved the way for augmented humans, esports, new forms of sociality, and new ways to engage the audience. Likewise, contextual factors push sports trends in two opposite directions simultaneously: on the one hand, they foster the indoorisation and individualization of sports; on the other hand, they encourage practicing sports outdoors and taking advantage of the “restorative environment” of nature. With this workshop, we would like to invite the MobileHCI community to discuss the current trends in portable technologies for sports and trace future directions for HCI research in this field.
Overview of the NLPCC 2022 Shared Task: Dialogue Text Analysis (DTA)

In this paper, we present an overview of the NLPCC 2022 shared task on Dialogue Texts Analysis (DTA). The evaluation consists of two sub-tasks: (1) Dialogue Topic Extraction (DTE) and (2) Dialogue Summary Generation (DSG). We manually annotated a large-scale corpus for DTA, in which each dialogue contains customer and service conversation. A total of 50 + teams participated in the DTA evaluation task. We believe that DTA will push forward the research in the field of dialogue text analysis.
Universal Physical Adversarial Attack via Background Image

Recently, adversarial attacks against object detectors have become research hotspots in academia. However, digital adversarial attacks need to generate adversarial perturbation on digital images in a "pixel-wise" way, which is challenging to deploy accurately in the real world. Physical adversarial attacks usually need to paste the adversarial patches on the surface of target objects one by one, which is not suitable for objects with complex shapes and is challenging to deploy in practice. In this paper, we propose a universal background adversarial attack method for deep learning object detection, which puts the target objects on the universal background image and changes the local pixel information around the target objects so that the object detectors cannot recognize the target objects. This method takes the form of a universal background image for the physical adversarial attack and is easy to deploy in the real world. It can use a single universal background image to attack different classes of target objects simultaneously and has good robustness under different angles and distances. Extensive experiments have shown that the universal background attack can successfully attack two object detection models, YOLO v3 and Faster R-CNN, with average success rates of 74.9% and 67.8% with varying distances from 15 cm to 60 cm and angels from $$-90^\circ $$ to $$90^\circ $$ in the physical world.
A New Training Algorithm Based on Finite-Time Stable Theory for Neural Networks

A new training algorithm based on finite-time stable theory is presented for neural networks in this paper. A new weight dynamic law is designed, two hyper parameters $$c_{1}$$ and $$\beta $$ are given out, and a new weight update algorithm is established. To verify performance of our new training algorithm, simulations of the classification problem of images in set CIFAR-10 by using VGG16 are considered. Some typical training algorithms such as SGD-M, AdaGrad, Adam and HJB integrated with them are compared to our algorithm. The simulating results show that our algorithm needs fewer epoches to converge and has superior training performance.
Stepwise Masking: A Masking Strategy Based on Stepwise Regression for Pre-training

Recently, capturing task-specific and domain-specific patterns during pre-training has been shown to help models better adapt to downstream tasks. Existing methods usually use large-scale domain corpus and downstream supervised data to further pre-train pre-trained language models, which often brings a large computational burden and these data are difficult to obtain in most cases. To address these issues, we propose a pre-training method with a novel masking strategy called stepwise masking. The method employs stepwise masking to mine tokens related to the downstream task in mid-scale in-domain data and masks them. Then, the model is trained on these annotated data. In this stage, task-guided pre-training enables the model to learn task-specific and domain-specific patterns simultaneously and efficiently. Experimental results on sentiment analysis tasks show that our method can effectively improve the performance of the model.
Towards Isolated AI Accelerators with OP-TEE on SoC-FPGAs

An artificial intelligence (AI) accelerator is a specialized hardware accelerator designed to accelerate machine learning applications. The machine learning applications may require an isolated execution for the confidentiality of model information and processing data and the integrity of the application tasks. For example, when critical applications such as biometrics use machine learning, the applications are required to execute in a trusted environment isolated not to be compromised by the other applications. The isolated execution of a machine learning application using an AI accelerator is often achieved with a proprietary hardware architecture consisting of dedicated security circuits for the accelerator. On the other hand, several previous works have proposed using open-source or general-purpose security functions for the isolation execution to reduce design costs and commonly apply to various accelerators. This paper proposes an isolated execution method of AI accelerators using OP-TEE, an open-source Trusted Execution Environment (TEE) implementing the Arm TrustZone technology. The contribution is to analyze the security threats of AI accelerators, propose the countermeasure based on OP-TEE, and evaluate the implementation of the isolated execution.
Towards Interpreting Vulnerability of Object Detection Models via Adversarial Distillation

Recent works have shown that deep learning models are highly vulnerable to adversarial examples, limiting the application of deep learning in security-critical systems. This paper aims to interpret the vulnerability of deep learning models to adversarial examples. We propose adversarial distillation to illustrate that adversarial examples are generalizable data features. Deep learning models are vulnerable to adversarial examples because models do not learn this data distribution. More specifically, we obtain adversarial features by introducing a generation and extraction mechanism. The generation mechanism generates adversarial examples, which mislead the source model trained on the original clean samples. The extraction term removes the original features and selects valid and generalizable adversarial features. Valuable adversarial features guide the model to learn the data distribution of adversarial examples and realize the model’s generalization on the adversarial dataset. Extensive experimental evaluations have proved the excellent generalization performance of the adversarial distillation model. Compared with the normally trained model, the mAP has increased by 2.17% on their respective test sets, while the mAP on the opponent’s test set is very low. The experimental results further prove that adversarial examples are also generalizable data features, which obeys a different data distribution from the clean data. Understanding why deep learning models are not robust to adversarial samples is helpful to attain interpretable and robust deep learning models. Robust models are essential for users to trust models and interact with the models, which can promote the application of deep learning in security-sensitive systems.
Anchor-Free Tracker Based on Space-Time Memory Network

In the visual object tracking task, the existing trackers cannot well solve the appearance of deformation, occlusion, and similar object interference, etc. To address these problems, this article proposes a new Anchor-free Tracker based on Space-time Memory Network (ATSMN). In this work, we innovatively use the space-time memory network, memory feature fusion network, and transformer feature cross fusion network. Through the synergy of above-mentioned innovations, trackers can make full use of temporal context information in the memory frames related to the object and better adapt to the appearance change of the object, which can obtain accurate classification and regression results. Extensive experimental results on challenging benchmarks show that ATSMN can achieve the SOTA level tracking performance compared with other advanced trackers.
Optimal Transport-Based Patch Matching for Image Style Transfer

State-of-the-art image style transfer methods have achieved impressive results by using neural networks. However, neural style transfer (NST) methods either ignore the local details of the style image by using the global statistics for style modeling or cannot fully use shallow features of neural networks, leading to the synthesized image having fewer details. In this study, we proposed a new patch-based style transfer method that directly operates in the image pixel domain without using any neural networks, achieving fascinating style transfer results with rich image details. The proposed method was derived from classic texture synthesis methods. Most previous methods rely on nearest neighbor search (NNS) for patch matching. However, this greedy strategy cannot guarantee the similarity of patch distributions between the synthesized image and the style image, which limits the expressiveness of textures. We solved this problem by proposing an optimal patch matching algorithm formed on the Optimal Transport (OT) theory, which theoretically guarantees the similarity of the patch distributions and gives a flexible style modeling method. Various qualitative and quantitative experiments demonstrated that the proposed method achieves better synthesized results than state-of-the-art style transfer methods, including NST and classic methods based on texture synthesis.