Reviewerly/exHarmony huggingface dataset:
Adaptive Progressive Image Enhancement for Edge-Assisted Mobile Vision

Recent advances in deep learning models have pushed Super-Resolution (SR) techniques to an unprecedented altitude, enabling high-quality image rendering with variable scaling size and natural fidelity. To deploy them on resource-constrained mobile devices, however, confronts significant chal-lenges of excessively long latency and poor user experience. To this end, we propose Apie, an edge-assisted adaptive image rendering system that allows low-latency, progressive image enhancement for a smooth user experience. Apie adopts a data parallel strategy across the end device and the edge server, along with a residual learning mechanism to judiciously retrieve information for SR models. Besides, a novel progressive image reconstruction is developed by exploiting content-aware image blocking and incremental image rendering, towards improved quality of user experience. Furthermore, Apie can dynamically adjust the choice of employed SR models with respect to the networking conditions, striking a good balance upon the latency-quality trade-off. Extensive evaluations show that Apie performs 7.33x faster than on-device GPU execution and 1.42x faster compared to the partial offloading method, while achieves 2.84dB higher PSNR compared to the interpolation method using conventional JPEG image compression and 0.74dB higher PSNR compared to the partial offloading method.
Improved ViT-Based Fine-grained Classification

The transformer architecture, originally used in the NLP tasks, is recently applied in computer vision tasks. The accuracy is improved when taking the ViT block into consideration. However, the improvement is limited since the Vit focuses on local details and breaks the global coherence of the image. In this paper, progressive multi-step training and knowledge distillation strategies are adopted in the vision transformer, preserving and providing more information from the macro view. The model progressively learns the information stored in the image crossing multiple granularities by adopting progressive training. After processing the whole image, the learned identifiable features flow into the self-distillation framework making it possible for massive data scale tasks. The experiments are conducted on several generally used datasets, and the output performance achieves a distinctive increase in accuracy when working on the CUB, CAR and AIR datasets compared to current state-of-the-art methods.
A Corporate Public Opinion Classification Method Based on Multi-teacher Knowledge Distillation

With the raising of media convergence, network public opinion monitoring system has become a necessary tool for government, enterprises and financial institutions to respond to public opinion events and create a favorable development environment. In recent years, pre-trained large-scale language models have established significant improvement in performance on a variety of natural language processing tasks such as text classification etc. However, the parameters of these large-scale models are at least hundreds of millions of, which makes it difficult to popularize. Therefore, knowledge distillation technology has attracted more and more attention as one of the ways to accomplish model compression. Based on public sentiment data of enterprises, this paper builds a three-teacher knowledge distillation deep learning framework to achieve the training of public opinion classification models with the same performance as large models at low computing costs as well as to pre-train large models in enterprise public opinion detection systems. An experiment in China shows that the model learned by knowledge distillation has slightly improved performance (Acc 2.45%) even under the premise of reducing the number of parameters by 41.58%, which illustrates the effectiveness of multi-teacher knowledge distillation network framework.
Research on Python Crawler Search System Based on Computer Big Data

This paper uses Python crawler technology, designed a commodity collection and analysis method. The system uses Python operation automation testing framework to simulate manual web browsing to bypass the complex anti-crawler encryption algorithm edited by complex JavaScript in some dynamic web pages and obtain the specified page information. This article uses the Requests library to download the resulting item information from searches by keyword. Both regular expressions and Beautiful Soup were used for initial cleaning of the data. Finally, the system stores the data into MongoDB database. Practice shows that Python language has very high feasibility and effectiveness, which not only ensures the implementation effect of web crawler framework, but also improves the performance of web crawler system. The system brings good user experience.
A Study on Innovative Smart City System with Blockchain Technology: Providing Better Living Environment for Humans

Blockchain technology is a secure decentralized data structure technique with no centralized system to take control. Each node in the blockchain plays an important role, where each node can check others’ node validity, create a block, and check the block and transaction integrity. There are many types of data produced in the smart city in different modules. The city's storage system needs a higher cost to improve and maintain system consistency and security. Furthermore, the cyberattack issue will expose crucial data to the attacker if the system security does not become comprehensive. In the blockchain system, nodes call “miner” to perform the consensus-based validation of the created block. Suppose all the miners in the smart city cannot verify the created block in the specified time interval. In that case, it causes high data latency in the smart city, and the participant in the smart city might operate abnormally. The paper aims to investigate the different factors in terms of nodes, miners and others that affect a smart city’s latency when the smart city integrates the new lightweight blockchain methodology known as blockchain manager concept. Lastly, examine the protection ability of different nodes from the DDoS attack on the smart city blockchain network.
Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining

Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining.Association rule mining aims to discover interesting correlations, frequent patterns, associations, or causal structures between items hidden in a large database.By exploiting quantum computing, we propose an efficient quantum search algorithm design to discover the maximum frequent patterns.We modified Grover's search algorithm so that a subspace of arbitrary symmetric states is used instead of the whole search space.We presented a novel quantum oracle design that employs a quantum counter to count the maximum frequent items and a quantum comparator to check with a minimum support threshold.The proposed derived algorithm increases the rate of the correct solutions since the search is only in a subspace.Furthermore, our algorithm significantly scales and optimizes the required number of qubits in design, which directly reflected positively on the performance.Our proposed design can accommodate more transactions and items and still have a good performance with a small number of qubits.
Deep Learning-Based Retrieval Algorithms for Recommendation Systems

The recommendation system is an important technology to solve information overload and capture users' interests, which is widely used in recommendation platforms such as e-commerce, movies, and books. The overall process of a recommendation system is that candidate items enter the recommendation system, a small number of items that users may be interested in are filtered by the retrieval layer, and then the final recommendation list is obtained by more precise filtering by the ranking layer. As the first stage of the recommendation system, the retrieval layer’s efficiency determines the ability of the recommendation system to quickly filter information, and it determines the upper limit of the ranking effect. However, few works have elaborated the retrieval layer in detail. Since the great achievements of deep learning techniques in recommendation systems, this paper focuses on the deep learning methods often used in the retrieval layer, which is of referenceable significance for the optimization of recommendation systems.
Approximate Shortest Distance Queries with Advanced Graph Analytics over Large-scale Encrypted Graphs

Understanding graph characteristics is of great importance for graph analytics. Among the many properties, shortest path distance is the fundamental and widely used one. With the advent of cloud computing, it is a natural choice for the data owners to host their massive graphs on the cloud and outsource the shortest distance querying service to it. However, the new paradigm brings serious security concerns as graph data and shortest distance queries may contain sensitive information of data owners and users. In this paper, we propose a novel scheme to support privacy-preserving approximate shortest distance queries with advanced graph analytics over large-scale encrypted graphs, which enables an untrusted cloud to answer shortest distance queries as well as advanced graph metrics (e.g., node centrality) without knowing the content of queries and the sensitive information of outsourced graphs. Compared with the state-of-the-art solutions, our design can support not only efficient and accurate shortest distance approximation, but also advanced graph analytics. We prove that our scheme is secure under the chosen-plaintext model. Experimental results over real-world datasets show that our scheme achieves high approximation accuracy with practical efficiency.
A Critical-Path-Based Vulnerability Detection Method for tx.origin Dependency of Smart Contract

Smart contracts are one of the most successfully applied technologies on the blockchain, which are decentralized and immutable. Smart contracts cannot be modified once deployed. Therefore, security detection of smart contracts before deployment is essential. Some smart contracts may have tx.origin dependency vulnerabilities. In this paper, we propose a critical path vulnerability detection method for detecting tx.origin dependency vulnerabilities in smart contracts. Then, in order to solve the problem that the traditional search algorithm cannot determine the critical path, we propose a path determination method based on path priority. Our method determines the critical path in the control flow graph, which enables us to detect the vulnerabilities existing in smart contracts more quickly. The experimental results show that our method is more efficient than the existing technology and the false positive rate is lower.
Low-Light Image Enhancement Under Non-uniform Dark

The low visibility of low-light images due to lack of exposure poses a significant challenge for vision tasks such as image fusion, detection and segmentation in low-light conditions. Real-world situations such as backlighting and shadow occlusion mostly exist with non-uniform low-light, while existing enhancement methods tend to brighten both low-light and normal-light regions, we actually prefer to enhance dark regions but suppress overexposed regions. To address this problem, we propose a new non-uniform dark visual network (NDVN) that uses the attention mechanism to enhance regions with different levels of illumination separately. Since deep-learning needs strong data-driven, for this purpose we carefully construct a non-uniform dark synthetic dataset (UDL) that is larger and more diverse than existing datasets, and more importantly it contains more non-uniform light states. We use the manually annotated luminance domain mask (LD-mask) in the dataset to drive the network to distinguish between low-light and extremely dark regions in the image. Guided by the LD-mask and the attention mechanism, the NDVN adaptively illuminates different light regions while enhancing the color and contrast of the image. More importantly, we introduce a new region loss function to constrain the network, resulting in better quality enhancement results. Extensive experiments show that our proposed network outperforms other state-of-the-art methods both qualitatively and quantitatively.
Realtime Sitting Posture Recognition on Embedded Device

It is difficult to maintain a standard sitting posture for long periods of time, and a non-standard sitting posture can damage human health. Therefore, it is important to detect sitting posture in real time and remind users to adjust to a healthy sitting posture. Deep learning-based sitting posture recognition methods currently achieve better improvement in recognition accuracy, but the models cannot combine high accuracy and speed on embedded platforms, which in turn makes it difficult to be applied in edge intelligence. Aiming to overcome the challenge, we propose a fast sitting posture recognition method based on OpenPose, using a ShuffleNetV2 network to replace the original backbone network to extract the underlying features, and using a cosine information distance to find redundant filters to prune and optimize the model. Lightweight model after pruning for more efficient real-time interaction. At the same time, the sitting posture recognition method is improved by fusing joint distance and angle features on the basis of skeletal joint features to improve the accuracy while ensuring the recognition detection speed. The optimized model can not only run at 8 fps on the Jetson Nano embedded device, but can also ensure recognition accuracy of 94.73 $$\%$$ . Experimental results show that the improved model can meet the real-time detection of sitting posture on embedded devices.
A Novel Attitude Feature Extraction Method for Multi-IMU Based Fall Detection System

Falling has become the leading cause of non-fatal and preventable injuries. The majority of existing fall detection systems (FDSs) rely on motion sensors. However, most of the applied feature extraction methods result in longer processing time and higher computation. This study proposes a novel attitude feature extraction (AFE) method that extract five feature sets from 54 raw measurements obtained from nine inertial measurement units (IMUs) placed on the firefighter’s protective clothing. Our results indicate that the metrics of attitude feature extraction (AFEM) method outperforms the existing metrics of raw feature extraction (RFEM) method in the fall detection. In addition, the proposed method reduces the algorithm processing time and computations significantly. This enables on-device fall detection classification on constrained processing architectures.
Research on Public Opinion Data Collection of Electric Power Information Based on Web Crawler Technology

As network communication becomes an important data foundation for the development and application of electric power. The rapid development of electricity public opinion will cause rapid spread and adverse effects if it is not controlled. Therefore, the collection of power network public opinion data has become an important research content in recent years. This paper uses web crawler technology to construct public opinion topic vectors. The web page similarity is calculated, and the public opinion topic data is crawled to realize the collection of public opinion data. By testing different numbers of web pages, the results show that the method in this paper has the advantages of high average recall rate, high accuracy rate, and less time-consuming data collection. It can effectively improve the efficiency of public opinion collection on the power network.
Face Recognition Based Beauty Algorithm in Smart City Applications

Recently, social networking software is widespread used to achieve intelligent life in the construction of smart city, and sharing selfies on social platforms has been a trend of interaction. As a result, beauty software as a popular tool has been widely welcomed by social media users. Along with the development of beauty technology, the popular beauty software in the market include Qingyan, Meitu, VSCO and so on, whose functions continue to be expanded as users demand for image beautification. However, those functions tend to be manually adjusted by users, which would easily result in facial asymmetry. To address this issue, the principles and key points of several classical beauty algorithms are thoroughly investigated in this paper, such as skin beauty, beauty makeup, image artistry, portrait deformation and sharpening filter. In the experiments, the 68 face feature points in Dlib library are used to perform face detection and locate feature points, and Gaussian filtering, bilateral filtering, local translation and bilinear interpolation methods are used to realize the functions of these beauty algorithms. The analysis results have been shown to demonstrate the efficiency of the investigations.
Automatic Smart Contract Generation with Knowledge Extraction and Unified Modeling Language

Since the launch of Ethereum in 2013, the smart contract has been a momentous part of the blockchain systems due to its character of automatic execution. The generation of smart contracts has also attracted extensive attention from the academic community. However, the preparation and generation of smart contracts are still mainly manual so far, which limits the scalability of the smart contracts. In this paper, we put forward a new method to generate smart contracts automatically based on knowledge extraction and Unified Modeling Language (UML), which can significantly accelerate the generation of smart contracts. We will describe this method in more detail based on the logistics supply chain.
Wipe Scene Change Detection in Object-Camera Motion Based on Linear Regression and an Inflated Spatial-Motion Neural Network

To facilitate content-based video analysis, automatic scene change detection (SCD) with large-scale motion activity is an essential fundamental step for locating a transition from one video scene to another.With the exponential increase in digital media usage, SCD has become more challenging in processing large motion content with minimal information loss and maximum perseverance.Wipe SCD in object-camera motion is noticeable evidence of this issue.Wipe transitions, which are a type of gradual transition, have diverse motion pattern changes when influenced by object-camera motion (camera pan, large-object, and zoom-in/out), creating a velocity imbalance in the same frame.Furthermore, this motion imbalance leads to false detection.Due to the loss of motion information and longer processing time of existing frameworks, we propose a novel method of wipe scene change detection (WSCD) based on deep spatial-motion feature analysis.First, large input videos are segmented into shots using dimensionality reduction and adaptive threshold.Secondly, linear regression is used to compute slope angle changes in shots for candidate selection and wipe localization.Finally, only selected candidates are processed to extract features using a two-stream inflated 3D-convolutional neural network for RGB stream and optical flow velocity for motion stream network (I3DCNN) and then classified into wipe in-motion and no-motion clips.The experimental results are obtained by classifying wipe patterns using a detection reviewing and merging strategy on corresponding wipe frames.The average improvement in wipe scene change detection accuracy evaluated on the benchmark TRECVID dataset is 11.9%, demonstrating the efficacy of our proposed method.
LowFreqAttack: An Frequency Attack Method in Time Series Prediction

Time series prediction has become an important research direction in data mining because of the time-varying pattern of data in various fields. However, time series prediction suffers from the problem of vulnerability to adversarial example attack, which leads to models making wrong decisions in critical application scenarios and causing great losses to people’s lives and properties. In addition, there is relatively little attacks research on time series prediction, and the existing attack methods simply migrate classical-attack methods in the image to time series prediction. On the one hand, it not only without fully considering the characteristics of temporal data but also without comparing and analyzing the effects of those classical-attack methods on time series prediction models. On the other hand, there is no comparative analysis of the effectiveness of these classical attack methods in different time series prediction methods. To address the above problems, this paper firstly compares the effectiveness of the attack methods on some time series prediction models and analyzes the inner mechanism of these time series prediction models. In addition, this paper finds that the defense ability of those models is related to their ability to portray the overall trend of time series data. Therefore, this paper further propose the new attack method, LowFreqAttack. The experimental results show that LowFreqAttack can attack the three existing time series prediction models better than the existing attach methods.
PV panel fault detection based on improved ResNet

At a time when photovoltaic power generation is becoming more and more mature, the maintenance of photovoltaic modules has also become a major problem. In order to avoid the environmental impact of photovoltaic hot spot damage photovoltaic panels, this leads to a decrease in power generation efficiency, as well as the current detection means small hot spots are often be missed, combined with the need for speed and accuracy in the UAV inspection and cleaning, for the traditional two-level detection method, a ResNet residual network-based full convolutional network model, and further use grayscale, filtering and edge gradient fitting and other measure to enhance the model evaluation conditions, achieving accurate hot spot location on PV panels.The experimental results show that the improved full convolutional network model is very useful for small photovoltaic panels. The experimental results show that the improved full convolutional network model is also effective in detecting small photovoltaic hot spots.The application of this model not only satisfies the requirements of PV inspection for real time, but also has a lower cost, stronger portability, can be applied to most scenarios and greatly reduces the requirements on the environment and the working state of PV modules.
Transfer Learning for Activity Recognition

Sensor-based human activity recognition (HAR) plays an important role in people’s daily life. HAR makes it possible to recognize people’s daily activities, thus monitoring people’s health status in a pervasive manner. In this chapter, we show how to use transfer learning for cross-domain human activity recognition on a public dataset. The complete code of this chapter can be found at: https://github.com/jindongwang/tlbook-code/tree/main/chap18_app_activity .