Reviewerly/exHarmony huggingface dataset:
Implementation of Blockchain Technology Using Hash Method in Java for Medical Record Application Development

Health problems are not only related to the availability of medical facilities and medical personnel but also include the security of health data. In this regard, the development of blockchain technology provides a bright spot for data protection. This group’s research is intended to discuss the application of blockchain technology in the health sector, to protect patient medical record data. Therefore, in this study, an analysis of several kinds of literature has been collected to find out how the blockchain works. The analysis results found that one of the blockchain elements is the hash. The hash function takes input using a variable length and then produces a fixed-length output. Using a hash algorithm, transactions that are taken as input, are output with a fixed size. The hash function as a blockchain element is then implemented using the Java language to protect medical record data when it is stored and shared among hospitals using medical record applications.
Neuromorphic Analog Implementation of Reservoir Computing for Machine Learning

In reservoir computing, dynamical systems are used to drive state-of-the-art machine learning with small training sets and minimal computing resources. Neuromorphic (brain-inspired) computing pose to further improve reservoir computing with energy-efficient spiking neural implementations. Here we propose an analog circuit design for reservoir computing using OZ spiking neurons, STDP (Spike-timing-dependent plasticity) synapses, and learning PES (prescribed error sensitivity) circuitry. We evaluated our design on a small scale using the Iris flower data set, demonstrating the potential application of neuromorphic analog hardware in reservoir computing.
Challenges and Benefits of Cloud Computing: Comparison Study

Cloud computing technology is the most widely used current application. Due to the strength of the infrastructure, the ease of providing services, and its speed, it is scalable and expandable, and it has many characteristics. Despite all that has been mentioned about cloud computing, it faces many problems. Like flexibility and security which is it makes an individual distrust computing when using it, and increases the concerns of individuals or companies over their data. In addition to cloud problems. However, there are significant challenges to solving cloud problems, the most important which is the challenge of flexibility, security, cost…etc. All the problems and challenges faced by cloud computing, have many benefits. Such as scalability, collaboration, speed, automatic update...etc. This paper discusses the most important problems and challenges that you face when using cloud computing. This paper also reviews the most important advantages of cloud computing.
Egocentric upper limb segmentation in unconstrained real-life scenarios

The segmentation of bare and clothed upper limbs in unconstrained real-life environments has been less explored. It is a challenging task that we tackled by training a deep neural network based on the DeepLabv3+ architecture. We collected about 46 thousand real-life and carefully labeled RGB egocentric images with a great variety of skin tones, clothes, occlusions, and lighting conditions. We then widely evaluated the proposed approach and compared it with state-of-the-art methods for hand and arm segmentation, e.g., Ego2Hands, EgoArm, and HGRNet. We used our test set and a subset of the EgoGesture dataset (EgoGestureSeg) to assess the model generalization level on challenging scenarios. Moreover, we tested our network on hand-only segmentation since it is a closely related task. We made a quantitative analysis through standard metrics for image segmentation and a qualitative evaluation by visually comparing the obtained predictions. Our approach outperforms all comparing models in both tasks and proving the robustness of the proposed approach to hand-to-hand and hand-to-object occlusions, dynamic user/camera movements, different lighting conditions, skin colors, clothes, and limb/hand poses.
Box Volume Measurement using Camera and ArUco Marker

This paper presents a volume measurement system based on image processing. The suggested plan offers reasonable costs, good accuracy, and convenience of manpower. The extra specifications like item alignment during measurement and putting the objects at a specific Place in the measurement area. Instead of using a single cell phone for the measurement, two cameras will be used, each of which will be positioned on a different plane to increase precision. To speed up processing, the application will be created on an Android platform and only be available on mobile devices. For precise measurement, images will be taken from two distinct perspectives (one from a top view and the other from a side view). First, a calibration procedure tailored to the problem at hand was carried out, and after that, the Hough transform was used throughout the image processing stage. The proposed method gives a rough volume measurement for objects with random shape even though it was originally designed to assess the volume of box-type objects. The system will yield satisfactory results when tested with different external disturbances.
Text mining: identification of similarity of text documents using hybrid similarity model

The volume of data that are accessible on the internet has increased dramatically. This growth of data will only increase exponentially in the future as more data exhaust devices are connected to the network. A part of these data consists of documents from various sources. As the data from various digital sources increase, it becomes tough to perform the process of identification of relevant information which is most essentially needed for their further usage. The goal of this research is to present a hybrid similarity algorithm that uses text summarization techniques to identify papers that are similar in terms of both semantic and contextual similarity. Some of these methods aim to quantify the corpus’s polysemy quotient using deep learning with numerous layers and prebuilt Natural Language Processing (NPL) models to determine document similarity. In comparison with other conventional algorithms, the experimental results of our model showed an accuracy of 76.25%.
Complementary Random Walk: A New Perspective on Graph Embedding

Random-walk based graph embedding algorithms like DeepWalk and Node2Vec are widely used to learn distinguishable representations of the nodes in a network. These methods treat different walks starting from every node as sentences in language to learn latent representations. However, nodes in a unique walking sequence often appear repeatedly. This situation results in the latent representations obtained by the aforementioned algorithms cannot capture the relationship between unconnected nodes, which have similar node features and graph topology structures. In this paper, we propose Complementary Random Walk (CRW) to solve this problem and embed the nodes in a network to obtain more robust low-dimensional vectors. By conducting a K-means clustering algorithm to cluster different features extracted from the graph, we can supply the original random walk with many other walking sequences, which consist of different unconnected nodes. And those nodes are sampled from the same cluster based on graph features, such as node degree, motif features, and so on. Our experiments achieve comparable or superior performance compared with other methods, validating the effectiveness of CRW.
A Separating Embedding Space Based Relation Network with RGB Modality Only for Cloth-Changing Person Re-Identification

The Cloth-changing person re-identification (CC-ReID) is more challenging than person re-identification (Re-ID) since the cloth-relevant features are unreliable. The current CC-ReID methods usually utilize some human parsing techniques such as semantic segmentation to guide the model to learn more cloth-irrelevant feature cues. However, the human parsing models are not necessarily reliable. For this, we propose a Separating Embedding Space based Relation Network (SESRN) for CC-ReID. Firstly, we use pairs of images as input of the model and consider the relation between them in SESRN instead of a single image that the existing CC-ReID models use. Secondly, we propose to separate the common feature embedding space outputted from the common backbone into two embedding subspaces including the cloth-irrelevant feature embedding subspace and cloth-related feature embedding subspace without using any human parsing techniques since we think that they are unreliable and introduce noise into model. Thirdly, we generate the different feature map weights on different subspaces or different comparison pairs on the same subspace, which is a simple and effective feature map visualization and analysis framework in CC-ReID. Finally, the extensive experiments show the effectiveness and robustness of our method.
Research on Dynamic Water Level Recognition of Cabin Based on Improved Retinex Algorithm

To accurately identify the dynamically changing water level in the ship's cabin, the Retinex algorithm is improved by using the bilateral filtering method firstly, which enhances the cabin image at the edge of the liquid level. then the image is preprocessed by image grayscale, image segmentation, and morphological processing, The PP-YOLO v2 algorithm is used to measure the water level with dynamic characteristics. Finally, the water tank is used to simulate the ship cabin, and the detection results of the algorithm proposed in this paper are compared with the traditional algorithm. The experimental results show that the improved Retinex algorithm combined with the PP-YOLO v2 algorithm has high accuracy in the dynamic water level recognition of the cabin, with a relative error of 0.71%, compared with the traditional algorithm, the combined liquid level recognition accuracy of this algorithm is improved by 2.09%, with strong application value.
Foreign Object Debris Detection Based on Gaussian Mixture Autoencoder of Pre-trained Features

In this study, a novel anomaly localization method called Gaussian Mixture Autoencoder of Pre-trained Features (GMAPF) is proposed to perform foreign object debris (FOD) detection in the field of aviation. GMAPF utilizes the pre-trained deep convolutional neural network to establish multi-hierarchical feature representations, which are then fed into the deep autoencoder for dimensionality reduction and learning of low-dimensional embedding for each pixel of an image. The distribution of the normal pixel embedding is then modeled by Gaussian mixture model (GMM). Besides, instead of Expectation-Maximization (EM), GMAPF leverages a multi-layer perceptron to learn the parameters of GMM. Therefore, GMAPF could simultaneously optimize the parameters of the deep autoencoder and GMM in an end-to-end way. Many experiments are done on a newly collected dataset FOD, and the experimental results demonstrate the validity of GMAPF.
MPSKT: Multi-head ProbSparse Self-Attention for Knowledge Tracing

Over the past two years, COVID-19 has led to a widespread rise in online education, and knowledge tracing has been used on various educational platforms. However, most existing knowledge tracing models still suffer from long-term dependence. To address this problem, we propose a Multi-head ProbSparse Self-Attention for Knowledge Tracing(MPSKT). Firstly, the temporal convolutional network is used to encode the position information of the input sequence. Then, the Multi-head ProbSparse Self-Attention in the encoder and decoder blocks is used to capture the relationship between the input sequences, and the convolution and pooling layers in the encoder block are used to shorten the length of the input sequence, which greatly reduces the time complexity of the model and better solves the problem of long-term dependence of the model. Finally, experimental results on three public online education datasets demonstrate the effectiveness of our proposed model.
Finding Key Training Data by Calculating Influence Score

Due to the complexity and opacity of decision models and increasing data volume requirements, this makes it more attractive to reduce data volume and improve model interpretability by selecting key data. In this paper, we propose an influence function-based method InfSort for data sorting and pruning, and demonstrate that the key data selected by this method outperforms an equal number of other data. In addition, we also found that the importance of the data is positively correlated with the speed and stability of the loss, and the key data is more conducive to speeding up the model convergence. We also developed a method CGT that prevents the risk of overfitting by controlling for the worst case distribution of the data. Experimental results show that our method is effective and efficient in emotion recognition tasks.
Chinese Machine Reading Comprehension Based on Language Model Containing Knowledge

Machine reading comprehension (MRC) is a task that requires machines to answer relevant questions based on a given context. In recent years, it has attracted extensive attention with the development of deep learning and big data. Considering that human beings will associate some external relevant knowledge when understanding the text, researchers have proposed a method of introducing knowledge outside the given context to assist reading and this method is called Knowledge-Based Machine Reading Comprehension (KBMRC). However, the current research on this method is still scattered, and the retrieval and fusion of relevant knowledge are still two challenges in application, especially in Chinese MRC. The contribution of this paper mainly on the following three points: Firstly, in order to resolve the problem of related knowledge retrieval, we build up a related knowledge set. Secondly, in order to resolve the problem of related knowledge fusion, we propose a negative sample generation strategy and train a language model containing knowledge. Finally, a twin-tower fusion model is constructed based on this model. The experiments on Chinese reading comprehension dataset CMRC2018 show that our method has a certain improvement compared with the baseline method without external knowledge.
Construction of Cross-Border Food Supply Chain Information Sharing Platform Based on Blockchain Under COVID-19

The pandemic of the COVID-19 has caused many problems in the cross-border food supply chain during transportation, such as food safety is difficult to guarantee due to the infection of people or environmental epidemics, information asymmetry and difficult to share in the food supply chain, and food related information is tampered with and difficult to trace during transportation. The emergence of blockchain technology has brought new solutions to the above problems. Based on the relevant functions and characteristics of the blockchain, this paper constructs a cross-border food supply chain information sharing platform under the blockchain technology, such as using smart contracts to monitor the epidemic security risk in the transportation process of the supply chain, using hash functions and tamper proof features to deal with food information fraud, and using the distributed and decentralized characteristics of the blockchain to solve the sharing problem of information asymmetry; The time stamp in the blockchain is used to trace the information of each link node of food and the initial node for accountability. In this paper, the construction process of the platform is described in detail from the perspective of model, function and subject, and the specific process of the platform is described in detail from the perspective of information storage, information sharing and information traceability. In this paper, the emerging blockchain technology is applied to all links of the food supply chain, and considering the current epidemic problems, the platform can bring new ideas to solve the problems of cross-border food transportation during the epidemic, and has certain theoretical value.
Research on the Secure Communication Model of Instant Messaging

People more and more concern about the security and usability of instant messaging applications as they have been widely used in our daily life and work. This paper analyzes the secure communication models of international popular instant messaging including message encryption as well as process of message sending and receiving in detail. For the aspect of usability, we present the principles of message forwarding in scenarios of group chat and multi-device online under different encryption modes. In addition, we design a secure communication model in the end-to-end encryption mode for multi-device online case, which effectively improves the security level of multi-device communication since the current encryption in this case only supports a weaker end-to-server mode. In fact, the secure communication model we designed is not limited to instant messaging, but also can be conveniently applied to communication scenarios with strict and necessary requirements for privacy protection, such as sensitive information of authorization and device configuration on multiple devices.
Identity-Based Deterministic Proxy Re-Encryption Scheme on Lattice

In the information age, it is necessary to improve the efficiency of data use and ensure the security and stability of data. This paper constructs an identity-based proxy re-encryption scheme based on the Learning with Rounding (LWR) problem on the lattice. The scheme uses the pre-image sampling technique to obtain the private key. When the message is encrypted, it is encrypted with a deterministic encryption algorithm. The algorithm encrypts each plaintext to generate a unique ciphertext, thereby improving the efficiency of encryption and decryption. In the proxy re-encryption scheme, the proxy is often semi-honest, so the homomorphic signature technology is introduced to detect whether the proxy calculates honestly and realize the verifiability of re-encryption. Finally, Honest Re-Encryption Attack (HRA) security, as an enhanced Chosen Plaintext Attack (CPA) security, can better capture the target of proxy re-encryption, enabling the constructed scheme to provide more robust security in practical applications.
Design of Embedded Trust Root Based on Dual-Kernel Architecture

Given the characteristics and design constraints of the embedded system, a software trust root construction method based on dual kernel architecture and composed of bootloader and trusted kernel and a stem branch trust chain transmission model are proposed ,aiming at the requirements of the trusted environment of embedded applications, The Bootloader, solidified in the boot FLASH, embeds the SHA-1 engine, to measure and load the trusted kernel. Meanwhile, the trusted kernel realizes the protection of the Bootloader by prohibiting the user kernel and upper-layer applications from writing access to the FLASH. The interaction between them, as the root of trust, can resist non-physical attacks; the trusted kernel provides password service-related functions for the user kernel; the application system and the user kernel where it is lockated run as a process of the trusted kernel. Finally, based on predicate logic, a formal proof of trusted boot is given, and a prototype system is built to verify the availability of the scheme.
Improved attention complementary dual channel network remote sensing image registration

An end-to-end registration algorithm based on deep learning is proposed to solve the problem of low accuracy and low efficiency of existing remote sensing image registration methods. Firstly, in the feature extraction stage, a double channel feature extraction network is proposed based on the principle of multi visual feature complementarity and the attention module, which makes the network pay more attention to the important channels and spaces of the image. Finally, in the matching phase, the feature relationship between the two directions is obtained by bidirectional correlation feature matching, which avoids the inaccuracy of single direction matching. Experimental results show that the proposed algorithm outperforms other algorithms on multiple datasets, and effectively improves the accuracy and efficiency of remote sensing image registration.
Artificial Intelligence in Entrepreneurial Mindfulness Using CRISP-DM Method

Entrepreneurship is an activity and process to generate added value in the economy. To support performance in carrying out entrepreneurial practices, an entrepreneur needs to develop entrepreneurial mindfulness. Entrepreneurial mindfulness is a concept based on cognitive construction so that it can influence identification and can proactively become the basis for the development of an entrepreneurial mindset. This study aims to build an artificial intelligence model that can predict the level of entrepreneurial mindfulness of users. To build an AI model, this research follows the workflow from CRISP-DM. CRISP-DM is one of the most popular lifecycles that is used for predictive analytics. There are four machine learning models that are used in this research, they are Random Forest, Adaboost, Gaussian Naïve Bayes and Deep Learning. By comparing the models using machine learning metrics, one best model can be used to be applied to the website. Accuracy, F1Score, Precision and Recall are used for evaluating machine learning models. At the end, Random Forest is used for predicting the level of mindfulness, because it has the highest values from all machine learning metrics (score of 0.95).
Remote sensing image registration based on fusion of local features and self-similar features

Remote sensing image registration is the core problem of further processing of remote sensing images, and has been widely used in urban development planning, ecological environment monitoring, geographical change detection and other fields. In this paper, a remote sensing image registration method based on fusion of local features and self-similar features is proposed. Firstly, the self-similar features are generated by the self-similar module, and then the local convolution features and the self-similar features are fused to reduce the influence of background clutter-induced distraction on the extracted features. Moreover, a improved parameter regression model that filters false matches is used to output the affine transformation parameters. Finally, the image to be registered can be transformed by the affine transformation parameters to achieve the intent of registration. The qualitative and quantitative assessments show that this method improves the registration accuracy of remote sensing image registration compared to the comparison methods.
Initial parameters of CNNs generated by Convolutional Sparse Representation with L1 error term

Convolutional Sparse Representation (CSR) approximates images with the convolutional sum of dictionary filters and corresponding sparse coefficients. To improve classification accuracy of Convolutional Neural Networks (CNNs), this paper proposes to use the dictionary filters generated by CSR as initial parameters of CNNs’ filters since the CSR filters express features of test images. Our method also estimates the error term of CSR with the L1 norm instead of the L2 norm to increase robustness against outliers in datasets for training. The results of experiments classifying CIFAR-10 show that the CNN using the initial parameters generated by the proposed method with the L1 error term shows the highest classification accuracy for small numbers of training images compared with the two methods: the proposed method with the L2 error term and the Xavier’s method.