Reviewerly/exHarmony huggingface dataset:
Financial Technologies for Accepting Transaction Using Cryptocurrency

Cryptocurrencies such as Bitcoin, Ethereum, and Litecoin are few examples of revolutionary Financial Technologies (FinTech) that are rapidly entering the finance market and transforming the global economy's power. Recently, cryptocurrency technology as an innovative technology has attracted attention and spread. Cryptocurrencies known as virtual currencies that have been widely used to buy and sell products and services over the internet, as well as a popular asset for hedging and speculation. However, current study and statistic shows that customers have high level of awareness but low acceptance towards cryptocurrency. In addition, the number of studies on cryptocurrency adoption in Malaysia also very less. Therefore, the aim of this study is to examines the accepting financial transaction using cryptocurrency from a customer perspective. The outcome of this study shows that (i) security and perceived ease of use are having a significant relationship with behavioral intention towards acceptance of cryptocurrency transactions; (ii) perceived trust and social influence are having no significant relationship with behavioral intention towards acceptance of cryptocurrency transactions. This study is important because it will help investors and potential customers to increase their knowledge in using and adopt cryptocurrency in their transaction.
Infrared image pedestrian detection based on deep convolutional neural network

Pedestrian detection in infrared images has been a hot and difficult research topic in computer version. Traditional methods of pedestrian detection mainly depend on the manual feature for the expression of human body and the results largely relies on the feature representation. Designing artificial features is time-consuming and labor intensive, requires heuristic expertise and experience. Deep learning model based on convolution neural network can automatically learn feature representation from the original images, while avoiding the drawbacks of artificial features. Its difficulty is the choice of network parameters. In this paper, we propose to use deep learning method based on convolution neural network in the process of pedestrian detection. In addition, we analyze the impact of network layers, convolution kernel sizes and feature maps to pedestrian detection in infrared images. The results demonstrate the superiority of our method over traditional methods in detection rate and alarm rate.
A Simple Algorithm for Submodular Minimum Linear Ordering

We consider the Minimum Linear Ordering Problem (MLOP): given a ground set N (of size n) and a set function f : 2N→ ℝ+ the goal is to find a permutation π over N that minimizes , where Nπ,i is the collection of the first i elements from N with respect to the permutation π. MLOP has been studied for various types of set functions, capturing multiple application, e.g., Minimum Linear Arrangement, Minimum Sum Vertex Cover, and Minimum Sum Set Cover. Surprisingly, for the general case of a symmetric submodular f an approximation of only O(n) is known and it is achieved by any permutation π. In this paper we present a simple ) approximation for MLOP for the case f is symmetric and submodular.
Subgraph Matching Based on Path Adaptation for Large-Scale Graph

To increase the accuracy and decrease the cost of subgraph querying in large-scale graph data, a subgraph matching method based on path adaptation is proposed. An index is built based on path RDF, and the query graph is decomposed into a set of paths to obtain a set of candidate paths for each path. Then, the candidate paths are concatenated together through K-Partition intersection. Experimental results show that the query accuracy is improved by 15% on average compared with SPATH and SPARRER when dealing with large-scale data.
Photovoltaic Panel Intelligent Management and Identification Detection System Based on YOLOv5

Photovoltaic power generation has significant energy, environmental protection and economic benefits. With the global attention to green energy, the development of photovoltaic power generation has become an inevitable trend. Photovoltaic panel assembly is a power generation device that generates direct current when exposed to sunlight, and is an important link in the photovoltaic power generation process. The geographic location of the photovoltaic panel, the user information to which the photovoltaic panel belongs, and the person in charge of the photovoltaic panel equipment are very important in the use of the photovoltaic panel, and need to be managed intelligently and efficiently. During the use of photovoltaic panels, photovoltaic panels need to undergo regular inspections to avoid affecting photovoltaic power generation output or causing safety accidents due to abnormal number and status of photovoltaic panel components. This paper builds a photovoltaic panel equipment intelligent management system to record photovoltaic equipment information in the power system. The system uses the YOLOv5 target detection model to realize image-based photovoltaic panel quantity identification and abnormality detection. The system compares with the equipment recorded information to give early warning of abnormal quantity and abnormal status. The advantage of the system proposed in this paper lies in the realization of efficient and intelligent management of photovoltaic panel information, high-precision identification of the number of photovoltaic panels, high-coverage detection of abnormal status, and real-time early warning of abnormal information.
Priv-IDS: A Privacy Protection and Intrusion Detection Framework for In-Vehicle Network

Intelligent connected vehicle (ICV) is equipped with advanced on-board sensors, controllers, actuators and other equipment of the new generation of vehicles, integrated with modern communication and network technology, to achieve intelligent information exchange and sharing. As an international standardized communication protocol, controller area network (CAN) plays an important role in vehicle communication. However, due to the CAN is plaintext broadcast communication, lack of encryption technology, CAN faces the challenge of malicious attack and privacy disclosure. In this paper, a machine learning method Priv-IDS based on local differential privacy (LDP) is proposed to protect the privacy of CAN data and detect malicious intrusion. The method performs random perturbation on CAN data and detects malicious attacks through temporal convolutional network (TCN). We propose a $$\alpha \beta $$ -LDP method, which ensures data availability as much as possible while protecting data privacy. This method provides a way to solve the problem of privacy disclosure caused by CAN data in machine learning intrusion detection. Based on the standard data set, this scheme is compared with other vehicle intrusion detection methods. The experimental results show that the proposed intrusion detection method is not different from other intrusion detection methods in terms of accuracy and time efficiency, but it is the first intrusion detection method to better protect CAN data based on LDP.
A Broad Learning System Based on the Idea of Vertical Federated Learning

As a new type of deep neural network, the broad learning system has been attracting attention since it was proposed. It shows good performance in image processing, face recognition, etc. To train a broad learning model requires comprehensive data. But the data on which the models are trained is often held by different companies and these companies are reluctant to share the data. To address this problem, we propose a broad learning system based on Vertical Federated Learning. Compared with the existing broad learning systems, it can integrate multi-party data to obtain comprehensive information, and realize the training of broad learning systems in the case of isolated data islands. This facilitates the application and advancement of broad learning systems.
USDSE: A Novel Method to Improve Service Reputation Based on Double-Side Evaluation

Fair evaluation of users is the basic guarantee for the healthy development of the service ecosystem. However, existing methods do not provide an indicator of when can get fair evaluation and how to reduce the proportion of malicious users from the root. This paper proposes a “user-service” double-side evaluation(USDSE) model to solve the problem above. Firstly, we start with getting the reputation of users by using the evaluation of service. Normal and malicious users are distinguished by their reputation. Secondly, we use the minimum number of normal users as the indicator to show when we can get fair evaluation. Finally, the revenue of employing collusive users has been analyzed to reduce the proportion of collusive users indirectly. The simulation experiments show that USDSE effectively improves the accuracy of identifying malicious users and reduces the revenue of employing collusive users.
Research on Detection Method of Large-Scale Network Internal Attack Based on Machine Learning

Aiming at the problem of inaccurate analysis of forged address attack behavior in current detection methods, this paper proposes a large-scale network attack detection method based on machine learning. A large-scale network internal attack detection model is constructed to analyze the degree of network internal attack. The model is trained by machine learning method to improve the convergence speed of model detection. After determining the attack target, analyzing the network vulnerability attribute, tracing the network internal attack source. Computes the hash of a function to ensure that the data set is complete and not tampered with. Alarm windows automatically check the point, set different alarm levels. The Euclidean distance is used as the distance measure of clustering process to distinguish the attack behavior. The detection formula of large-scale network internal attack is constructed, and the calculation result is divided into the level of large-scale network internal attack. Experimental results show that the method is consistent with the actual path, and the highest recall is 93%, the highest precision is 0.98, with good detection results.
Data Reconstruction from Gradient Updates in Federated Learning

Federated learning has become an emerging technology to protect data privacy in the distributed learning area, by keeping each client user’s data locally. However, recent work shows that client users’ data might still be stolen (or reconstructed) directly from gradient updates. After exploring the attack and defense techniques of these data reconstruction methods, we discover that the attacker cannot steal the victim’s data unless it has prior knowledge about the victim’s data size. Thus, the attacker can hardly reconstruct any useful information without these prior knowledge. In this paper, we provide a novel data reconstruction method to obtain a high-dimensional compressed data from the gradient updates, without these prior knowledge. Experiment results show that our reconstructed data can be used to attack the model, with high attack accuracy.
Data Leakage with Label Reconstruction in Distributed Learning Environments

Distributed learning is commonly applied for the high demands of computation resources while training models with large-scale data. However, existing solutions revealed that it may lead to information leakage of private data. Attackers are able to reconstruct data via minimizing the difference between shared gradients and fake gradients that are produced by themselves. This is so-called data leakage attack from gradients. We find that, in many cases, such attack can only obtain random noises or extreme fuzzy information, which are quite different from the raw data. To deal with this problem, we exploit the gradient updates as well as their labels during the parameter-server training, and discover significant information losses during the label coding. Thus, in our method, we reconstruct the labels of samples with a fine-grained method, and we recover the raw inputs with these processed labels. We test our method on different datasets (i.e. CIFAR100 dataset and solid color images) and obtained the reconstructed results which are obviously clearer than the ones of the state-of-the-arts. The results prove the effectiveness of our algorithm in fixing defects to some extent as well. Extensive experiments are also conducted to explore defense strategies against data leakage.
Thrill-K Architecture: Towards a Solution to the Problem of Knowledge Based Understanding

While end-to-end learning systems are rapidly gaining capabilities and popularity, the increasing computational demands for deploying such systems, along with a lack of flexibility, adaptability, explainability, reasoning and verification capabilities, require new types of architectures. Here we introduce a classification of hybrid systems which, based on an analysis of human knowledge and intelligence, combines neural learning with various types of knowledge and knowledge sources. We present the Thrill-K architecture as a prototypical solution for integrating instantaneous knowledge, standby knowledge and external knowledge sources in a framework capable of inference, learning and intelligent control.
The Construction and Development of Vocal Music Art Guidance Course in Colleges and Universities

Music an art discipline, can enable people in different regions to communicate with each other.Such a world unified language has always occupied an important position in the history of human culture.In the actual construction and development process, the music education in colleges and universities reflects more diversified development models.Improving the students' music quality and comprehensive quality has always been the core of the construction and development of music and vocal music art guidance courses.In teaching, teachers should pay more attention to cultivating students' different attempts in music.This article takes the vocal music art guidance course and the construction and development ideas as the main discussion content, hoping to find a way to improve the efficiency of students' vocal music art appreciation in music teaching.
The Learning Motivation and Educational Guidance of Undergraduates in Agriculture-related Colleges and Universities

Learning motivation is the internal driving force that directly affects students' learning status, and it is also an important prerequisite for students to acquire professional skills and improve their comprehensive literacy.This paper conducts extensive research on undergraduates in seven different disciplines in an agriculture-related university, and analyzes their learning motivation.Analyzed problems such as lack of depth and unclear understanding of the coordinated development of morality, intelligence, body, beauty, labor, etc., and gave relevant countermeasures and suggestions.
The Comparative Study of Imitation of the Guqin in Three Chinese Piano Works

The guqin is a plucked string instrument with more than 3,000 years of history in China.Many composers have explored piano music imitating the musical elements (i.e. the properties distinguishing different styles, eras, composers, regions, and pieces from one another such as rhythm, melody, harmony, timbre, dynamics, texture and form) of the guqin and produced many piano works inspired by the guqin.The effects differ in the creative techniques of such piano works across different periods.This paper takes three piano pieces -'Yangguan San Die ' (composed by Li Haiying in 1978), '#F Shang: Calligraphy and Qin rhyme' (composed by Wang Lisan in 1981) and 'Qin' (composed by Ge Ganru in 1986) -as examples to study how the piano can, in different ways, imitate the musical elements of the guqin.Comparative analysis is used to assess the three piano pieces and explore the sublimation of the guqin within Chinese piano creations in so pianists can gain a deeper understanding of Chinese piano works influenced by the guqin.
Modality-specific Adaptive Scaling Method for Cross-modal Retrieval

There are huge differences in data distribution and feature representation of different modalities. How to flexibly and accurately retrieve data from different modalities is a challenging problem. The mainstream common subspace method only focus on the heterogeneity gap between modalities, and use a unified method to jointly learn the common representation of different modalities, which can easily lead to the difficulty of multi-modal unified fitting. In this work, we innovatively propose the concept of multi-modal information density discrepancy, and propose a modality-specific adaptive scaling method incorporating prior knowledge, which can adaptively learn the most suitable network for different modalities. Comprehensive experimental results on three widely used cross-modal retrieval datasets show the proposed MASM achieves the state-of-the-art results and significantly outperforms other existing methods.
Improving Text Classification for Auditing Application Using Adversarial Training and Chinese Pretrained Model

In the information audition application of Chinese enterprises, people use a text classification model to audit certain information as qualified or not. However, the existing text classification model often ignores the language characteristics of Chinese, such as polysemy issues and Chinese having no natural breaks. At the same time, the information to be audited in a Chinese enterprise is often irregular, which often leads to problems such as misjudgment and poor robustness of the model. Given the above problems, we proposed an AT-NEZHA (Adversarial Training NEZHA) classification model based on the combination of adversarial training and the Chinese pretrained model. On the one hand, the word embedding of the NEZHA model is used to fuse context information to solve the problem of polysemy and word segmentation. On the other hand, the adversarial training algorithm is used to perform gradient perturbation on the parameter matrix of the word embedding layer to increase the loss in the training process. Thus, the model can find more robust parameters to improve the robustness and classification performance. In SODIC2021 (Global Open Data Application Innovation Competition), the effectiveness of the method was verified on the enterprise security-hidden-danger data of the track of ‘Text Mining-based Enterprise Hazard Detection Quality Analysis Model”. Comparing with the most famous Chinese pretrained model of the basic version BERT -base-Chinese, the F1 value of our proposed model AT-NEZHA is improved by 2.2 % from 94.2 % to 96.4%. The experimental results show that AT -NEZHA can effectively improve the performance of the text classification.
Network Security Situation Prediction Based on CCQPSO-BiLSTM

In order to improve the prediction accuracy and speed of the model, a network security situation prediction model based on chaotic cross quantum particle swarm optimization optimization bidirectional long short term memory (CCQPSO-BiLSTM) network is proposed. Logistics is used to map the initial population as a chaotic sequence to search for the optimal solution, and the information exchange of individuals in the population is carried out through the vertical crossover operation. Finally, the hyperparameters of the model are optimized by CCQPSO. Through four standard test functions, the effectiveness of introducing chaotic mapping and crossover operations into quantum particle swarm optimization is verified. The experimental results show that the fitting degree of the proposed prediction method can reach 0.9977, and the convergence speed is greatly improved compared with the comparison algorithm.
Marina Roggero, Le vie dei libri. Letture, lingua e pubblico nell’Italia moderna, Bologne, Il Mulino, 2021, 294 p.

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Feasibility Study of English Teaching in Higher Vocational Colleges Based on 'POA'

The output-oriented method is widely used in college English teaching activities, which mainly reflects the handling of the problem of the disconnection between input and output in the college English teaching model, which is often referred to as the separation of learning and use.To solve the problem of separation of learning and use, it is necessary to analyze from the two directions of teaching concept and teaching practice, and it is necessary to clarify what to learn, how to learn and what is the purpose of learning.This article mainly discusses the feasibility of output-oriented method in higher vocational English teaching activities.
Recent Extensions of Robert F. Lusch's “Big Picture” Thinking

This abstract highlights Bob Lusch's stature in the profession, followed by a summery of four papers that extend Service-Dominant Logic originally developed by Lusch and Vargo. Three of Bob Lusch's ca-authors highlight their very positive interactions with Lusch. The abstract concludes with a brief synopsis of the Eulogy delivered at Bob's Funeral.
A New Generation of String Simulations

The main objective of this thesis was to build a simulation capable of tackling more realistic and/or more exotic strings at resolutions adequate for deriving reliable constraints with future observational data. This would of course require extreme hardware resources and more optimized simulations than had been available thus far.
An improved dingo optimization algorithm based on periodic convergence factor strategy

Aiming at the shortcomings of Dingo Optimization Algorithm (DOA), such as low precision, less diversity of population and weak global searching ability, an improved dingo optimization algorithm based on periodic convergence factor strategy is proposed. The convergence factor is incorporated into the location update of group attack, persecution attack and sweep of the basic DOA, and the convergence speed and accuracy of the algorithm are obviously improved. By solving the low-dimensional and Galway test functions, the experimental results verify the optimization performance of the improved algorithm, which can be concluded that IDOA has stronger optimization ability and faster convergence speed.
Improved Local Vector Pattern Descriptor for Face Recognition

With the rising demands of visual observation systems, vehicle and public recognition at a distance has gained extra notice for the researchers in recent times. Real-world face recognition systems require cautious balancing of two important concerns: Elapse Time, recognition rate. In this paper Improved Local Vector Pattern (ILVP) feature extraction technique and Nearest Neighbor (NN) classification techniques are worked out to improve the recognition rate as well as to enhance the computational time. The Improved Local Vector Pattern (ILVP) computes the values between the adjacent pixels and β reference pixels in various distance D and different direction for every pixel. A micropattern is created with respect to the reference pixels using Comparative Space Transform (CST). CST is used to encode the spatial information of a face image into binary pattern. The binary patterns generated using CST is lower when compared to the number of binary patterns generated using Local Multi Code Pattern (LMCP). ILVP generates 8(8×1) binary patterns for each pixel. These binary patterns are collected by histogram bins. ILVP outperforms the existing LVP for ORL face dataset.