Reviewerly/exHarmony huggingface dataset:
Characterisation of Cryptoassets as a Separate Category of Digital Assets

Transactions with cryptoassets are a social trend due to unconventional trading and greater security, but also due to the exceptional profitability of certain types of cryptoassets. Despite the reality of their existence, insufficient regulation of private law relations with regard to such assets is noticeable in most legal systems, which is the cause of legal uncertainty. Different types of cryptoassets do not have equivalent content. Exchange tokens do not incorporate any right of an obligatory character, while utility tokens have a link to the holder’s right to access goods or services. From the perspective of comparative law, security tokens are particularly interesting. Although its civil law is fundamentally based on the pandect system, German law has defined crypto securities as things (res). On the other hand, Swiss law defined ledger-based securities as rights. There is an obvious conflict between various legal approaches conditioned by legal traditions and readiness for private law reforms. Therefore, it is necessary to consider defining the legal nature of cryptoassets in a more appropriate manner, i.e., the rights which exist as related to cryptoassets and, in particular whether there are exclusive rights of an obligatory character or perhaps even the rights in rem.
A Graph-Based Efficient Service Composition Method for Computer Aided Engineering (CAE)

Complex CAE (Computer Aided Engineering) logic in the network can be implemented by combining existing services, but the network environment changes dynamically, and different service instances of the same function have different performances. In this paper, we propose a graph-based efficient service composition method. Using the service discovery method based on the shortest path in the graph, a calculation method of service matching degree is proposed in service selection, which solves the problem of partial matching of service relationships and the dynamics of service instances. Experiments show that the improved algorithm reduces the running time and improves the search efficiency on a dataset for the same size.
A Improved Prior Box Generation Method for Small Object Detection

As a task in object detection, small object detection mainly focuses on detecting objects of small size, which is more complex than general object detection. It is pivotal in various applications, e.g., small tumor detection, national defense and security, and traffic surveillance. Small objects have low pixels, few effective features, and a large influence of background noise, making small object detection extremely challenging. Currently, most object detection algorithms fail to take advantage of global context information to improve accuracy. Moreover, the conventional bounding-box proposal generation method will cause the missed detection of small targets since the target features are few and challenging to locate. To address the above problems, this paper uses the position enhancement method to improve the proposals’ generation to improve the recall rate and accuracy rate of small object detection. First, the location enhancement module adds additional keypoint location supervision to obtain target latent keypoints. Besides, we propose a keypoint expansion method to get more accurate keypoint locations. A global contextual attention mechanism is further introduced, enabling the detector to learn fine-grained features with contextual location information better. The experimental results on two datasets show that the proposed method can significantly improve the accuracy and recall of small target detection.
Context-Aware Quaternion Embedding for Knowledge Graph Completion

In this paper, we study the learning representation of entities and relationships in the link prediction task of knowledge graph. The knowledge graph is a collection of factual triples, but most of them are incomplete. At present, some models use complex rotation to model triples, and obtain more effective results. However, such models generally use specific structures to learn the representation of entities or relationships, and do not make full use of the context information of entities and relations. In addition, in order to achieve high performance, models often need larger embedding dimensions and more epoches, which will cause large time and space cost. To systematically tackle these problems, we develop a novel knowledge graph embedding method, named CAQuatE. We propose two concepts to select valuable context information, then design a context information encoder to enhance the original embedding, and finally use quaternion multiplication to model triples. The experiment and results on two common benchmark datasets show that CAQuatE can significantly outperform the existing state-of-the-art model in the knowledge graph completion task by obtaining lower dimensional representation vectors with fewer epoches and no additional parameters.
Deep Reinforcement Learning-Based Scheduling Algorithm for Service Differentiation in Cloud Business Process Management System

Business process management system in cloud is increasingly used in enterprise organizations for its advantages of flexibility, affordability, and usability. The service differentiation problem for cloud business process management system has not been thoroughly investigated, nevertheless. Service differentiation requires corresponding service quality for different service level agreements. To address this problem, this paper designs a scheduling system in cloud business process management system by combining characteristics of the workflow engine, models the request scheduling process as the Markov decision process, and proposes a request scheduling algorithm for service differentiation in cloud business process management system based on deep reinforcement learning. The algorithm effectively reduces the over-provisioning of service quality while maintaining a low service level agreement violation rate, thus enhancing the service differentiation effect and conserving system resources. Experiments illustrate that the algorithm achieves a better service differentiation effect with a 74.4% drop rate in service quality over-provisioning compared to the heuristic algorithm while maintaining the service level agreement violation rate within 5%.
Visual Scene-Aware Dialogue System for Cross-Modal Intelligent Human-Machine Interaction

Adequate perception and understanding of the user’s visual context is an important part of a robot’s ability to interact naturally with humans and achieve true anthropomorphism. In this paper, we focus on the emerging field of visual scene-aware dialogue system for cross-modal intelligent human-machine interaction, which faces the following challenges: (1) The video content has complex dynamic changes in both temporal and spatial semantic space, which makes it difficult to extract accurate visual semantic information; (2) The user’s attention during multiple rounds of dialogue usually involves objects at different spatial positions in different video clips. This makes it necessary for the dialogue agent to have fine-grained reasoning capabilities to understand the user dialogue context; (3) There is information redundancy and complementary among multi-modal features, which requires reasonable processing of multi-modal information to enable the dialogue agent to gain a comprehensive understanding of the dialogue scene. To address the above challenges, this paper proposes a Transformer-based neural network framework to extract fine-grained visual semantic information through space-to-time and time-to-space bidirectional inference; and proposes a multi-modal fusion method based on the cross-attention framework, which enables multi-modal features to be fully interacted and fused in a cross manner. The experimental results show that compared with the baseline model, the model in this paper improves 39.5%, 32.1%, 19.7%, and 61.3% in the four metrics of BLEU, METEOR, ROUGE-L, and CIDEr, which represent the fluency, accuracy, adequacy, and recall of the generated conversation contents, respectively.
Scene Adaptive Persistent Target Tracking and Attack Method Based on Deep Reinforcement Learning

As an intelligent device integrating a series of advanced technologies, mobile robots have been widely used in the field of defense and military affairs because of their high degree of autonomy and flexibility. They can independently track and attack dynamic targets. However, traditional tracking attack algorithms are sensitive to the changes of the external environment, and does not have mobility and expansibility, while deep reinforcement learning can adapt to different environments because of its good learning and exploration ability. In order to pursuit target accurately and robust, this paper proposes a solution based on deep reinforcement learning algorithm. In view of the low accuracy and low robustness of traditional dynamic target pursuit, this paper models the dynamic target tracking and attack problem of mobile robots as a Partially Observable Markov Decision Process (POMDP), and proposes a general-purpose end-to-end deep reinforcement learning framework based on dual agents to track and attack targets accurately in different scenarios. Aiming at the problem that it is difficult for mobile robots to accurately track targets and evade obstacles, this paper uses partial zero-sum game to improve the reward function to provide implicit guidance for attackers to pursue targets, and uses asynchronous advantage actor critic (A3C) algorithm to train models in parallel. Experiments in this paper show that the model can be transferred to different scenarios and has good generalization performance. Compared with the baseline method, the attacker’s time to successfully destroy the target is reduced by 44.7% at most in the maze scene and 40.5% at most in the block scene, which verifies the effectiveness of the proposed method. In addition, this paper analyzes the effectiveness of each structure of the model through ablation experiments, which illustrates the effectiveness and necessity of each module and provides a theoretical basis for subsequent research.
Horizontal Federated Traffic Speed Prediction Base on Secure Node Attribute Aggregation

Federated graph learning has been widely used in distributed graph machine learning tasks. The data distribution of existing graph-based federated Spatio-temporal prediction methods is mainly segmented by graph topology. However, in the real-world Spatio-temporal traffic speed prediction task, a location will have data from different devices belonging to different companies. A node may have multi-party information in the real-world distributed traffic speed prediction scenario. The difference in multi-party information leads to the information not being fully utilized. Moreover, the direct transmission of node embedding in the federated learning process may also risk privacy leaks. Using homomorphic encryption and other encryption methods will bring a high computational overhead. Therefore we propose a new distributed privacy-preserving traffic speed prediction algorithm, which uses secure node attribute aggregation strategy(SNAAS) to apply to the multi-party collaborative traffic speed prediction scenario when the graph topology structure is public. At the same time, secret sharing technology is used in SNAAS to protect the attribute matrix and reduce the overhead of secret computing.
Instance-Aware Style-Swap for Disentangled Attribute-Level Image Editing

Recent studies have shown that attribute-level image editing could be achieved by modifying the latent code of style-based Generative Adversarial Network (StyleGAN). However, many existing methods suffer from semantic entanglement, which leads to undesirable changes when editing a specific attribute. To solve this problem, we focus on the $$\mathbb {S}$$ space of StyleGAN2 and propose a simple and effective Optimization-based Instance-Aware Style-Swap method, which could perform a natural transformation effect on arbitrary source images and reference images by searching instance-aware swap coefficients with the guidance of prior information. By further analyzing the learned swapping coefficients, we can identify the style channels connected with a specific target attribute. We verify the effectiveness of our proposed methods in the field of face attribute editing. Extensive experiments have demonstrated that our method can achieve controllable and fine-grained image editing along various attributes. Qualitative and quantitative results show the advantages of our method compared with other semantic image editing methods.
Question Answering System Based on University Knowledge Graph

The Question Answering (Q &A) system can recognize natural language questions and give an answer, and people can get the information they want accurately and quickly from the Q &A system. The SCHOLAT is an academic social network. It has a University Portrait Project, which includes information on more than 2,700 colleges and universities in China. In this paper, we build a Q &A system for the university information field on this basis. We construct a university Knowledge Graph using the university database as a knowledge base for the Q &A system. We also collect more than two thousand university-related questions as the training data for the Q &A model. The Q &A system first performs entity recognition of the input question to obtain the abstract formula of the question sentence and a list of entity vocabulary. Secondly, a feature vocabulary extraction operation is performed and a word vector is constructed. Thirdly, the word vector is input to a Bayes classifier to obtain the type of question (i.e., intent). Then, structured query statements are selected according to the question type and the entity vocabulary list is used as query parameters to obtain relevant knowledge data from the Knowledge Graph. Finally, this knowledge data is used to construct answer sentences and output them.
Active Authorization Control of Deep Models Using Channel Pruning

In recent work, researchers have proposed deep neural network(DNN) model active authorization control protection strategies. Because active authorization strategies can prevent attackers from stealing models in advance, they have become a focus of DNN model copyright protection research. At present, most active authorization methods significantly reduce the accuracy by encrypting the parameters of a DNN model or modifying the structure of a DNN model, to prevent malicious infringers from using the model. The active authorization method of modifying the structure of a DNN model impacts the original task accuracy of the DNN model. Moreover, in active authorization methods that encrypt DNN model parameters, authorized users need to perform many calculations to decrypt the DNN model parameters. Therefore, this paper uses a channel pruning algorithm to control the authorization of the DNN model. In this work, the pruning rate or threshold is used as the secret key of the DNN model, the secret key is used to prune and fine-tune the original DNN model before the DNN model is distributed to authorized users, and the fine-tuned DNN model can restore performance similar to that of the original DNN model. Due to the advantages of the pruning mechanism, the DNN model retains the performance of the original task during the active authorization process, and reducing the number of calculations. We perform our work with the CIFAR-10 and CIFAR-100 datasets, and the experimental results show that we only need to prune a small number of channels in the DNN model to determine whether a user is authorized.
Chinese Event Extraction Based on Hierarchical Attention Mechanism

Event extraction try to extract structured event information from unstructured text. Its major researches can be summarized into three categories: classification methods based on feature learning, methods based on question answering and methods based on Seq2seq. All of these methods are difficult to deal with multi-event sentence. In order to solve this problem, we use hierarchical attention mechanism to treat event extraction as a relation classification task. The BERT and CRF are first used to identify candidate triggers and arguments, and then the hierarchical attention mechanism is used to identify the relationship between trigger and argument. Experiments on both ACE2005 and CEC show that our method outperforms in both trigger classification and argument classification.
A Survey of Computer Vision-Based Fall Detection and Technology Perspectives

With the increase in the number of elderly people living alone, the use of computer vision technology for real-time fall detection is of great importance. In this paper, we review fall detection based on computer vision from four perspectives: background significance, current research status, relevant influencing factors, and future research outlook. We summarized our approach by classifying the three types of input image data in fall detection systems: RGB (Red, Green, Blue), Depth, and IR (Infrared Radiation), outlining research in both target tracking and bone detection for basic image processing tasks, as well as methods for processing video data. We analyzed the possible effects of multiple factors on fall detection regarding camera selection, the individual object recognized, and the recognition environment, and collected the solutions. Based on the current problems and trends in vision-based fall detection, we present an outlook on future research and propose four new ideas including functional extensions using the easy fusion feature of Mask R-CNN (Mask Region with Convolutional Neural Network), the use of YOLO (You Only Look Once) family to improve the speed of target detection, using variants of LSTM (Long Short-Term Memory) such as GRU (Gate Recurrent Unit) to achieve more efficient detection, and using Transformer methods that have been migrated from natural language processing to computer vision for detection.
ICKG: An I Ching Knowledge Graph Tool Revealing Ancient Wisdom

The I Ching was originally a book of divination in ancient China. After thousands of years of deriving and redefining, it has been transformed into a profound book of dialectical philosophy with a series of philosophical thoughts and intellectual wisdom. However, due to its rich and special knowledge, it is difficult for traditional methods to mine deeper knowledge. This article first gives a brief introduction to the I Ching. Then, according to the knowledge structure of the I Ching, the classes of entities such as hexagrams, Yin and Yang lines, and words in the I Ching are defined, and the relationships between entities in the same class are also defined, and the relationships between entities different classes are constructed to establish the I Ching Knowledge Graph. Through the I Ching Knowledge graph, knowledge reasoning and link mining can be realized. In addition, an interactive tool (ICKG) is developed to support knowledge graph-based visualization and interactive knowledge mining. It provides users with search and visualization functions for each hexagram and each entity in the I Ching. Moreover, it supports the visualized linking including the connections among entities in the knowledge graph and the relationships among the image of the 64 hexagrams. This provides not only an interactive platform for scholars better understand the I Ching, but also a powerful toolkit to mine the deep knowledge in the I Ching.
Research on User's Mental Health Based on Comment Text

With the rapid development of society and the rapid pace of life, people’s psychological pressure is aggravated, which leads to a surge in the number of patients with mental diseases, and has an impact on the society. In order to better understand people’s mental health status, many experts and scholars have researched and designed a series of psychological assessment questionnaires, but most of these questionnaires are set out in the form of judgment and selection. Patients with mental illness can hide their real mental health status by choosing seemingly correct options. In this paper, based on the emotions of the dictionary user mental health system, select BosonNLP emotion as basic emotional lexicon dictionaries, combined with the praise and criticism dictionary + negative word dictionary + emoji dictionary + degree adverbs dictionary have the comment text sentiment analysis, the classification of positive and negative results as discriminant psychological abnormal emotional characteristics of the user, in order to analyze the mental health status of users. After evaluation and analysis on the microblog comment text dataset, it is found that the classification accuracy of the sentiment dictionary constructed based on this paper is better than that based on BosonNLP sentiment dictionary.
Learning User Embeddings Based on Long Short-Term User Group Modeling for Next-Item Recommendation

Session-based recommender systems are increasingly applied to next-item recommendations. However, existing approaches encode session information of each user independently and do not consider the interrelationship between users. This work is based on the intuition that the dynamic groups of like-minded users exist through the time, and users in the same group might share a similar preference. By considering the impact of latent user groups, we can learn a user’s preference in a better way. To this end, we propose a recommendation model based on learning user embeddings by modeling long and short-term dynamic latent user groups. It not only captures the latent group information of users, but also perceives the change of user grouping with time. Specifically, we utilize two network units to learn users’ long and short-term sessions, respectively. Meanwhile, we employ two additional units to detect which latent groups a user belongs to, followed by an aggregation of these latent group representations. Finally, user preference representations are shaped comprehensively by considering all these four aspects, based on an attention mechanism. Extensive experiments prove our model outperforms multiple state-of-the-art methods in terms of Recall, mAP, and AUC metrics.
Aesthetics-Diven Online Summarization to First-Person Tourism Videos

Nowadays video blog (vlog) has gradually become popular. In scenarios such as tourism, many vloggers use mobile terminals to record first-person videos, which have redundant information, complex scenes and continuous shots. Such videos bring inconvenience to the creation of vlogs. The technology of video summarization can extract the main information of original videos. However, these works usually rely on large computing resources, which are not applicable for this scene. This paper focuses on artistic video summaries of first-person perspective videos in tourism scenes, and at the same time takes the resource constraints of terminal devices into account. And we propose an aesthetics-driven online summarization method of the first-person tourism video. In the first stage, the model uses Canny operator and HoG features to quickly generate summaries online. And in the second stage, it further extracts video segments based on the aesthetics like color and symmetry of images. The results of experiments show that this method can effectively carry out video summarization and aesthetic processing, and the prototype based on the method was implemented with an APP.