Reviewerly/exHarmony huggingface dataset:
An Optimized Model based on Metric-Learning for Few-Shot Classification

Few-shot learning makes up for the shortcomings of traditional deep learning that requires a large amount of labeled data, and has great potential in promoting machines to become more intelligent. Many existing few-shot learning methods have achieved benign performance in numerous classification tasks by training a classifier, yet some trained models are restricted by shallow networks which will gravely restrict their feature expression ability. In addition, what proves awful is that some previous few-shot learning methods do not use appropriate loss functions to train excellent models, which limits their performance to some extent. To settle above problems, we optimize the classical few-shot learning framework, that is, prototypical networks, from three aspects: data augmentation, increasing the network's feature expression ability and improving the training loss function. It is worth mentioning that besides keeping simple and efficient, our innovative metric-learning-based few-shot classification framework is capable to be integrated into the same model to achieve end-to-end training. Immense amounts of experimental results show that our model not only performs well in classification tasks, but also shows its amazing superiority and competitiveness compared with related technologies.
Review of Secured, Robust and Imperceptible Digital Watermarking Scheme

In the last two decades, there is an incredible evolution in network and multimedia technology. This advancement also makes easier for unauthorized users to replicate, retransmission, changing reproduction and upload the contents in an illegal way. The increased and widespread usage of digital images has aroused great concerns from malicious attackers. The technology of Digital watermarking provides the methods to overcome the mentioned issues. Therefore, the proposed paper delivers in-depth information about image watermarking. This review paper covers literature review on digital image watermarking covered from various papers. In addition to reviewing many International, National journals, articles and many conferences papers are also taken in this review paper. This paper deals with entire digital image watermarking process from the scratch as the definition of digital image watermarking, their types, characteristics, robustness parameters and the associated literature. The comparative analysis extracts the drawback, deficiencies and limitations of the digital image watermarking process by extracting the research gaps. The ultimate aim of the proposed review is to find the still existing challenges by providing an environment for researchers to tell them a lot more work required in this field. The challenges are categories based on various attacks operators, quality of watermarked image, watermark capacity, false detection rate and security aspects of watermark and watermark process.
Hough Circle Detection Method for Shaking Instruments in Drilling Field

At present, the real-time performance of data acquisition has become the key to the development of intelligent drilling. Due to the limitation of stay wire and power supply, the stability of data transmission is not high. In view of the data cannot be collected effectively, machine vision is introduced into the drilling field to automatically judge and read the instruments, there are often fuzzy images of vibration instability in the acquired instrument images. By analyzing the reasons and rules of instrument jitter, a new scheme is proposed. The dithering image is blurred by Wiener filter, and the template matching is made for the initial frame picture. The disc and the center of the circle are obtained from the matched stable image by improved Hough transform, and then they are drawn on the deblurring image. The experiment shows that the error of the circle obtained in this scheme is more stable than the traditional algorithm.
Compression algorithm for live face recognition model based on depth-separable convolution

In this paper, we present a new compression algorithm for live face recognition models. Firstly, a VGG11 convolutional neural network is improved based on depth-separable convolution. The depth-separable convolution layer consists of a depth convolution layer and a point-by-point convolution layer. The depth convolution is used for filtering, acting on each channel of the input. And the point-by-point convolution is used for transforming the channels, acting on the output feature mapping of the deep convolution. Compared with the original model, although the accuracy of the improved model decreases by about 1%, the model size reduces to about 1/5 of the original size. Secondly, since the float16 quantization approximates continuously valued floating-point model weights to a finite number of discrete values with a low loss of accuracy, the model volume is compressed by half with little impact on model accuracy through float16 quantization. The proposed method can compress the original face live recognition model by 90.94% and reduce the recognition time by 83.46% with 1.07% accuracy loss.
System support and mechanisms for adaptive edge-to-cloud DNN model serving

We present an orchestration scheme for Deep Neural Network (DNN) model serving, capable of computation distribution over the device-to-cloud continuum and low-latency inference. Our system allows automated layer-wise splitting of DNN structures and their adaptive distribution over compute hosts, providing an execution environment for collaborative inference. Model deployment and its self-adaptation at runtime are implemented by optimization algorithms supported in a plug-in manner. These follow service and infrastructure provider criteria and constraints, expressed via well-defined interfaces. Our framework can serve diverse neural architectures, including DNNs with early exits, with zero to minimal modifications.
TridentShell: a Covert and Scalable Backdoor Injection Attack on Web Applications

Web backdoor attack is a kind of popular network attack, which can cause a serious damage to websites. In practice, cyber attackers often exploit vulnerabilities in the system or web applications to implant a backdoor to a web server. To address this challenge, static feature detection is believed to be an effective solution. However, it may also leave a potential security “hole” that could be exploited by intruders. In this paper, we propose a novel backdoor attack method called TridentShell, which can inject a webshell into the memory of web application server without leaving attack traces. Our attack is able to bypass almost all types of static detection methods. In particular, it attempts to blend itself into the web server and erase attack traces automatically, instead of encrypting or obfuscating the content of webshell to avoid detection. Besides, TridentShell can still be executed even when the webmasters restrict the access to web directory. In the evaluation, we showcase how TridentShell can successfully inject a webshell into five different types of Java application servers (covering around 87% Java application servers in the market), and can remove the attack traces on the server (increasing the detection difficulty).
Using Inductive Transfer Learning to Improve Hotel Review Spam Detection

The buying decisions of consumers across the world are increasingly influenced by online reviews. With this in mind, there exists increased opportunity for unscrupulous business owners or spammers to create false reviews to promote their brand or smear those of their competitors. As a result, there have been many studies of ways to identify review spam. However, a common limitation in these studies is the lack of large amounts of labeled training data. This is problematic because Deep Learning is susceptible to overfitting when trained on small datasets. In this paper, we empirically explore the use of inductive transfer learning to help with the problem by using a language model trained on Wikipedia and fine-tuning it for the task of hotel review spam detection. We show that it is possible to use inductive transfer learning to create a deep learning model that outperforms traditional bag of words based approaches on datasets with limited labeled data.
Quantum Image Representation on Clusters

We propose a novel scheme for quantum image representation using the cluster states (QIRC). This scheme is capable to represent images of any size and color. We emphasize the use of cluster states in this scheme due to its high entanglement and various experimental realization across several hardware. We also propose a secure image encryption scheme embedded with QIRC. Further, we discuss the possibility of QIRC to be implemented successfully with a real quantum device in the NISQ era, by executing example representations of a 2×2 and a 3×2 binary image on a real quantum processor provided by the IBM Quantum.
Comparative Analysis of Hands-free Mouse Controlling based on Face Tracking

Computer and mouse are two devices that inseparable from each other. Because mouse movement will control cursor movement to do any task that occurs on the computer, our research will also replace the role of the mouse in controlling cursor movement, using facial tracking by HOG and Haarcascade. Using facial movements instead of the mouse to move the cursor, users can minimize hand movements so users with impaired hands can operate the computer without a mouse. It is called hands-free. We use the HOG and Haarcascade method to determine the difference in time required by each method to control user movement. Here we experiment with 12 participants to find out the difference in time and accuracy. We use ANOVA analysis to produce a significant time difference and accuracy between those two methods. The accuracy shows that HOG has better accuracy than Haarcascade. HOG's accuracy is about 95.79%. In addition, age category analysis also affects the time generated. From this age category, it turns out that it produces a significant difference.
A Light Anchor-Free Detection Network for Remote Sensing Images via Heatmap-Saliency Distillation

Object detection in remote sensing (RS) images is a challenging task because of complex background and multi-scale objects. Recently, much research has been devoted to improving detection accuracy, but they ignore the speed and memory size. In this letter, we introduce a light anchor-free detection model for resource-limited satellite devices trained with the proposed heatmap-saliency distillation (HSD) strategy, enhancing the performance of small models by learning teachers’ heatmaps in output layers and middle layers. On the one hand, the students mimic the teacher’s localization prediction of negative targets indicating the object shapes. On the other hand, saliency coefficient maps are generated by the ground truth heatmaps with rectangular masks to help students obtain features of multi-scale objects and local contexts across the complex background. Significantly, the model with a Res-9-256 backbone achieves 94.60% mAP on the NWPU VHR-10 dataset with only 8.5 MB of memory. And the test time of this tiny network performs 16.2 ms per image. Additional experiments are conducted on the DOTA dataset. Comprehensive evaluations demonstrate the effectiveness of our light anchor-free object detector trained with the HSD method.
Intelligent Learning Processes

Learning refers to the process where students obtain knowledge, skills, and attitude through interaction with teachers, peers, and instructional information. With the development of AI technology, it has been applied in many aspects of the learning process. For example, knowledge recommendation algorithm can recommend the most suitable learning resources for learners, and knowledge tracking model can monitor the learning status of learners.
Research on Integration of Education and Teaching Data Resource Sharing Platform Based on Blockchain Technology

With the deepening of China's education reform, openness, sharing and integration have become an important development direction, and also an important foundation and guarantee for promoting education reform, innovation and development. Big data, cloud computing and Internet of Things are emerging computer technologies, which promote people's society to develop in a more advanced direction. As the gathering place of talents and the launching place of scientific innovation, universities have abundant resources of education, teaching and scientific research. Universities shoulder the heavy responsibility of personnel training, and should adapt to the development trend of the times, make full use of new technologies such as big data and Internet of Things, improve their teaching management quality, and actively build smart campuses. This paper introduces the education big data resource sharing platform, discusses the platform construction strategy from the platform structure and platform system, and discusses the platform operation mechanism based on blockchain technology from the aspects of integration, classified storage, open access to resources and platform operation development, aiming at strengthening the sharing of education big data resources in the new era and promoting the development of education in China.
Analysis of Techniques for Data Mining

Data mining has been a popular branch of computer science in recent years with great impact in different areas. It is a technique to find the inner connection, the unexpected pattern from a large amount of data and to obtain certain broad conclusions from it. Currently, many data mining techniques are developed and used in order to process data from various aspects and to improve the speed and accuracy of data analysis. Considering that data mining techniques combine knowledge from multiple domains, this article will present an overview of temporal techniques for data mining.
A literature survey on anti-phishing in websites

Abstract. Phishing and fraud sites have been widespread on the internet in recent times, which's become a source of great concern and a serious cybersecurity problem, as internet fraudsters target sensitive data and personal information of users, especially the username and password. Numerous approaches have been proposed and used to prevent and reduce these phishing websites and attacks, and protect users and their privacy. In this paper, we categorized the present anti-phishing approaches into two main classes: Content-based and Non-content-based. The content-based approach is also classified into URL content analysis and webpage content analysis. This helps in finding out numerous anti-phishing techniques and algorithms to choose the best approach in future contributions.
A Monadic Framework for Real-time 2D Multi Person Pose Detection for captured Images and Videos

In Computer Vision Field, the Real-time 2D Multi Person Human Pose Estimation (MPHPE) plays a vital role in empowering machines to have a perception of persons in captured videos. In this work, we introduce a framework for 2D multiperson pose detector. The proposed method utilizes non- parametric depiction that we termed as Part-Affinity- Fields (PAF) machine can learn these fields to connect body parts of persons in an image. It uses bottom-up approach system to accomplish high precision with real-time performance, in spite of number of individuals being tracked in an image. In earlier methods, in training stage the PAFs and part detection were refined concurrently. We show that only PAF enhancement is pretty much better than enhancement of both PAF & body part location brings results in a significant optimization in both execution time and precision. Not only has that it also kept the accuracy of each body part individually. This paper also reports the comparison of results along with the method with earlier methods. This method is also suitable for real time applications that can be run on embedded system with improved speed.
Reasoning over multiplex heterogeneous graph for Target-oriented Opinion Words Extraction

Target-oriented Opinion Word Extraction (TOWE) is a new emerging subtask of Aspect Based Sentiment Analysis (ABSA), which aims to extract fine-grained opinion terms for a given aspect term from a sentence. In this task, the key point is how to find the correct opinion that is far away from its corresponding aspect. Ideally, reinforcement learning (RL) seems to be a promising approach due to its delayed reward mechanism. However, as aspect-opinion interaction data is likely to be complicated, it is not easy to directly apply RL techniques to improve the performance. In this paper, we propose a novel Padding-Enhanced Reinforcement learning model (PER) to address this issue. Specifically, PER first designs a multiplex heterogeneous graph to cover both sequential structure and syntactic structure in order to enrich their interactions and alleviate the long distance issue. By formulating the extraction task as a Markov Decision Process (MDP), PER then walks on the designed graph to infer corresponding opinions for each aspect. In addition, a padding module is further designed to aggregate rich information from distant nodes to guide the exploration process. Extensive experimental results on four widely used datasets illustrate that our proposed model consistently outperforms the state-of-the-art methods.
Reasoning on Health Condition Evolution for Enhanced Detection of Vulnerable People in Emergency Settings

During an emergency event, such as a fire evacuation, support services benefit from having information about people who may require special assistance. In this context, health data represents a particularly important source of information, as it can allow an emergency response system to build an accurate picture of people's relevant health conditions and use this to advise responders. However, to perform this task, a system needs to represent and reason over the evolution of health conditions over time. Crucially, it needs to predict the probability that a potentially relevant condition mentioned in a health record is still valid at the time of the emergency. In this paper, we propose a methodology for representing the evolution of health conditions and reasoning about them in the context of an emergency scenario. To support our approach with data, we develop a pipeline to capture knowledge about condition evolution from reliable sources in natural language. We incorporate these two components into a system that predicts a person's likelihood of being vulnerable during an emergency event. Finally, we demonstrate that representing and reasoning about condition evolution improves the quality and precision of the recommendations provided by our system to emergency services.
A high-precision laser displacement measurement system

The rapid development of modern science and technology puts forward higher and higher requirements for displacement measurement. As a non-contact measurement technology, laser ranging has played an important role in various fields for the unique advantages and has become a hot topic in the field of measurement research. At present, there are little research on high-precision displacement measurement at medium and long distance, the needs of which are widespread in practical application, so this work has important value. Among various laser ranging technologies, the laser triangulation method has the advantages of high-speed, high precision, simple structure, etc. Therefore, this paper chooses the direct laser triangulation method to carry out the research after analyzed the common types of laser triangulation system. In view of the requirements of high-precision displacement measurement requirements at medium and long distance, this article discussed the design of laser triangulation measuring system and the selection of structural parameters. Then we proposed a direct laser triangulation measurement structure under Scheimpflug conditions. This system selects PSD displacement transducer to collect the position information of the scattered light spot. After that, this paper discusses the principle of laser triangulation distance measurement and simulates the system. Then the influence of different parameters on high-precision displacement measurement and the relationship between them are investigated. Based on the research talked above, we got a set of structural parameters of the displacement measurement system. The measurement accuracy can be ~micron with the working distance of 500mm.
Intelligent support and command system based on WAPI terminal equipment

Abstract In order to solve the problem that the process of WAPI terminal connection is troublesome, this paper proposes an intelligent support and command system of WAPI terminal equipment based on centralized control AC + AP architecture. The system stores WAPI certificate on AC (centralized controller), WAPI terminal equipment is associated with AP (wireless access point), and AP equipment notifies AC of the associated events of the terminal, AC equipment and terminal equipment enter authentication and complete certificate authentication. Complete access authentication through AC and terminal equipment, and store the unicast and multicast keys generated by authentication negotiation on AC. This method is more convenient to maintain the certificate. At the same time, on the premise of ensuring the security of the certificate, it also improves the encryption and decryption efficiency and improves the user’s business experience.
Responsible Technology Design: Conversations for Success

Abstract Digital humanism calls for new technologies that enhance human dignity and autonomy by educating, controlling, or otherwise holding developers responsible. However, this approach to responsible technology design paradoxically depends on the premise that technology is a path to overcoming human limitations while assuming that developers are themselves capable of super-human feats of prognostication. Recognizing developers as subject to human limitations themselves means that responsible technology design cannot be merely a matter of expecting developers to create technology that leads to certain desirable outcomes. Rather, responsible design involves expecting the technologies to be designed in ways that provide for active, meaningful, ongoing conversations between the developer and the technology, between the user and the technology, and between the user and the developer—and expecting that designers and users will commit to engaging in those conversations.
A Scheme to Improve SNR of Received EMI Signal from Information Display Device

This paper analyzes the characteristics of the display signal for an information display device and proposes a synchronization algorithm using its periodicity. The synchronization is the most important to enhance the signal-to-noise ratio (SNR), and it requires two kinds of phase information. One is the phase of the frame, and the other is the phase of the carrier frequency. Hence, this paper describes a methodology to estimate the phase terms, and the result conducted measurement data is verified.