Reviewerly/exHarmony huggingface dataset:
TCNNn Text Classification Model based on nAdam Optimizer

With the rapid development of mobile Internet, complicated information appears in people's life in various forms. As one of the main carriers of information, text plays the role of information transmission bridge in life, but in the face of mixed data, relying on computers to efficiently process text has become an excellent and necessary choice. This paper proposes a TCNNn (Transformer+CNN+nAdam) model based on the improved Adam optimizer and the integration of Transformer mechanism. By learning and calculating the contribution size of input data through the attention mechanism, the autocorrelation of each word in the text is established. A classification experiment is carried out on the news text data set. In addition, in order to further verify the effectiveness of the model, ablation experiment was also carried out at the end of this paper. The results of both the comparative experiment and ablation experiment confirmed that the model did improve the text classification evaluation index compared with the baseline model, which verified the effectiveness of the model.
Research on Key Technologies of Applying White Box Network Equipment in Cloud-Network Collaborative Service System

This article studies the key business requirements of cloud data centre network virtualization, and analyses the problems existing in the existing Overlay SDN data centre network virtualization products. At the same time, a general SDN system solution based on Open Daylight and white box switches to support network virtualization of heterogeneous cloud platforms is proposed and the corresponding system implementation is given.
A pedestrian tracking algorithm based on improved DeepSORT

In order to overcome the problem of false tracking caused by similar pedestrian head information in the process of pedestrian tracking, a pedestrian tracking algorithm based on improved DeepSORT is proposed. Number of pedestrian head ID exchanges. The double-line counting algorithm is used to count the entry and exit of pedestrians, which improves the problem of wrong counting caused by pedestrians wandering in the video. The experimental results show that the average tracking accuracy of the improved algorithm on the two video sequences is 86.2%, which is 4.6% higher than that of the DeepSORT algorithm, which is of great significance for the subsequent pedestrian count.
Introduction and Discussion on Quantum Communication and Quantum Network

Being one of the major points of research focus since the end of the twentieth century, quantum computation technologies have experienced significant improvements and is under the progress of forming an inter-connected commercial quantum network in recent years. Communication through quantum mechanisms proved to be considerably more efficient and secure than classical communication protocols. Basic quantum communication networks have the primary composition of quantum processing nodes and channels where the interaction between the nodes is satisfied by the entanglement phenomenon. Quantum networking technology is under rapid development currently and is anticipated to receive milestone achievements shortly.
Deep Learning Techniques in Financial Fraud Detection

Nowadays, the rapid development of information technology brings great convenience to people and the financial industry. However, hidden fraud risks emerge at the same time. We can find increasing financial fraud easily. A typical example is credit card fraud, and it will cause the loss of billions of dollars for financial companies and institutions every year. In the process of solving these new types of financial frauds, some traditional statistical and machine learning methods don't perform well. As a result, lots of experts devote themselves to designing some new machine learning methods, and they do have found some great new methods. This paper gives a comprehensive review of new statistics and machine learning methods in detecting financial fraud. The review summarizes and introduces the core idea of these new methods. It provides a good reference source in guiding the detection of financial fraud for both academic and practical fields with useful information on the statistic and machine learning fields.
Research on Gaussian Calculation Simulation Reaction Model based on Computer Big Data

In order to meet the real-time requirements of the online modeling algorithm, the adaptive natural gradient method (ANG) is proposed in the training of the Gaussian process, which is an online Gaussian process regression modeling algorithm based on the adaptive natural gradient method. This paper proposes an effective learning algorithm of Gaussian mixture model. Using this algorithm to process simulated video data and real video, the results show that the learning algorithm proposed in this paper is superior to the previous methods in terms of convergence rate and accuracy.
Implicit Annealing in Kernel Spaces: A Strongly Consistent Clustering Approach

Kernel k-means clustering is a powerful tool for unsupervised learning of non-linearly separable data. Its merits are thoroughly validated on a suite of simulated datasets and real data benchmarks that feature nonlinear and multi-view separation. Since the earliest attempts, researchers have noted that such algorithms often become trapped by local minima arising from the non-convexity of the underlying objective function. In this paper, we generalize recent results leveraging a general family of means to combat sub-optimal local solutions to the kernel and multi-kernel settings. Called Kernel Power k-Means, our algorithm uses majorization-minimization (MM) to better solve this non-convex problem. We show that the method implicitly performs annealing in kernel feature space while retaining efficient, closed-form updates. We rigorously characterize its convergence properties both from computational and statistical points of view. In particular, we characterize the large sample behavior of the proposed method by establishing strong consistency guarantees as well as finite-sample bounds on the excess risk of the estimates through modern tools in learning theory. The proposal's efficacy is demonstrated through an array of simulated and real data experiments.
Hand keypoint detection with super resolution

Hand interaction is an important research content in computer image processing at present. In sign language recognition, social interaction, virtual reality and augmented reality, the hand is the main input device for human interaction.Aiming at the problem of low recognition rate of hand keypoint in video, this paper proposes a deep convolutional neural network to recognize hand keypoint in video. The neural network is divided into two parts, the first part is image super-resolution, the purpose is to improve the resolution of each frame in the video, so that the image of each frame is clear, to have a high-resolution input image; The second part is the detection model, in order to ensure the real-time performance of hand keypoint detection, the model adopts a lightweight network structure to detect hand keypoint. The results show that this method has a high accuracy rate for the hand keypoint in the video, and the model was tested on the test set. Experimental results show that after adding super-resolution, the hand keypoint detection in the video is significantly improved.
House Price Prediction Model Using Bridge Memristors Recurrent Neural Network

In recent decay, the house price prediction plays important role because of it's the volatile of house price which makes significant impact on property valuation and economic growth. It characterizes are attracted the numerous researchers, businessman and people who buy or sell house towards it. The volatile of house price is occurred based on various factors like location, facility, neighborhood, etc. In this way, researchers are evaluating the factors using machine and deep learning process to analysis the information. Although, regression-based analysis has problem due to its nonlinear and linear information in neural network. Thus, we have proposed a novel Bridge Memristors Recurrent Neural Network to forecast the house price prediction in this paper. In addition, RBP algorithm is used on Bridge Memristors RNN for train the neural network in efficient manner. Besides, our proposed model carried out outstanding performance than existing models to attain the high prediction rate by analyzing the correlation coefficient.
Architecture Analysis of Energy Information Sharing Platform for New Power System

Under the trend of new power system construction and digital transformation, the power system generates a large amount of data flow. It is urgent to build an energy power system information sharing platform to promote the efficient allocation of resources and maintain the orderly operation of the power system. This paper studies an energy information sharing platform for new power systems, and designs in detail from the overall architecture, development platform architecture, access architecture, deployment architecture, and functional architecture. The platform is established based on advanced information and communication technologies such as "Cloud Computing, Internet of Things, Mobile Internet, Big Data" and cloud-edge collaboration. By integrating functional modules such as coordination and optimization decision-making module, continuous power supply monitoring module, and unified information model, the construction of energy information sharing platform and system integration are realized. The platform integrates "edge technology + multi-type communication + unified platform + data fusion + business intelligence", realizes the deep integration of digital technology and physical electrical system, realizes the intelligent interaction and precise control of source, network, load and storage under the new power system.
Data Analysis of Mutual Interference between FM Signal and Spread Spectrum Signal

In this paper, the problem of the frequency FM signal and the frequency amplifier signal is certain, and the interinterference of the FM signal and the amplifying signal is analyzed in this paper, and the interinterference of the FM signal and the spread signal is analyzed. The research shows that the different signal strength and frequency of the interference signal have a certain effect on the phase of the useful signal and the error rate. Through the interinterference experiment, the analysis of the corresponding interference laws is analyzed, and the best adjacent frequency and signal strength of the smallest effect are found, and the reference is provided for the actual project.
Signal generator and signal generation method for continuous long record wave playback power source

This article studies a signal generator and signal generation method for continuous long-wave recording and playback power source. It uses a 20bit DAC based on a segmented structure, which can achieve a maximum of 20bit amplitude resolution, compared with existing signal generators. The solution greatly improves the signal resolution and has a simple structure. At the same time, there is no need to add other operational amplifiers and DAs, which greatly improves the linearity of the signal generator; with the single reference bipolar output mode of the DAC, the positive and negative sides of the signal generator are eliminated Distortion error caused by value asymmetry; a method for generating continuous recording and playback signals is proposed, which can realize the generation of long-term, non-periodic signals, and the duration of recording and playback is convenient, and the theoretical length is unlimited.
On Design of Covert Communication System with Frequency Hopping by OFDM

With the rapid development of computer networks, network information security has gradually been concerned. The research on covert channel has become one of a hot issue in information security. However, the secure communication with encryption may attract much attention of the adversaries due to the random cipher messages. Therefore, the covert communication technology becomes a hot issue, since the covert communication can escape the monitor of the manicous users. In this paper, we have proposed a novel covert communication system by using frequency modulation to transmit covert information. With the of orthogonal frequency division multiplexing (OFDM) scheme, we propose to embedded the covert bits with frequency hopping. Furthermore, in the covert communication system, we put forth that the frequency of random jitter is employed as the carrier of covert messages, and the spread spectrum is encoded to encrypt the covert information. Simulation results show that the proposed covert communication system can achieve a good performance on bit error ratio and reliable covert communication.
Specifying and Verifying SDP Protocol Based Zero Trust Architecture Using TLA+

Software Defined Perimeter (SDP) is a prevalent security framework designed by Cloud Security Alliance (CSA) for Zero Trust Network Access (ZTNA) which aims to prevent novel threats in cloud environment. But there are few studies on the verification of correctness and security properties of SDP architecture through rigorous mathematical methods, thus we utilize formal methods to study its correctness and security. This paper aims to specify and verify the security and correctness properties of SDP framework using TLA+. First, an extensible formal model for SDP protocols with verification framework is built according to CSA's specification and the specific implementation of the popular open-source project called fwknop. This model not only specifies the behaviors of standard access control entities defined by SDP protocols but also includes the specification of cyber attack behaviors launched by hackers aiming at system security and correctness. Then this model is implemented using a formal specification language called TLA+ and relevant security and correctness properties are verified via the model checking tool called TLC. The verification results show that current SDP protocol framework has a security flaw in the scenario of remote access through NAT technology. To fix this problem, we further propose an improved scheme for SDP framework and update the TLA+ model accordingly for re-check. The final experimental results prove that the improved scheme can mitigate the risk of the vulnerability efficiently.
The Application Research of Zabbix in Intelligent Network Information Center

With the continuous development of informatization, the network information center bears more and more heavy tasks, the maintenance pressure is increasing, for the management of a variety of hardware equipment and information system performance monitoring requirements are urgent. Zabbix as a distributed monitoring, control and management system, based on the mature distributed architecture design and development on market, can be all sorts the open source visual components, implementation of various monitoring data graphical display of mobile phones, state of early waring and analysis of the maintenance staff to have good support.
Combination Strategy Standard Recommendation Algorithm for Standard Information System

To fulfill the standard recommendation requirements of the enterprise's standard information system, this paper proposes a combination strategy standard recommendation algorithm, which is based on factors such as standard name, standard classification number, and user historical standard data. Then some experiments are conducted that use China national standard data and compared with standard recommendation algorithms based on content feature and collaborative filtering. The experiment results show the combined strategy standard recommendation algorithm can improve the recommendation effect to a certain extent.
Three-dimensional path planning of UAV based on ant colony optimization

This paper propose an improved ant colony path planning algorithm for the disadvantages of slow convergence, low efficiency and easy local optimum. First, the 3D space is modeled by the 3D elevation map method, and the 3D space is divided into two directions, horizontal and vertical, which effectively improves the efficiency of path planning; then, the pheromone update rules and search strategies in the ACO are improved. Finally, carry out simulation experiments. The results show that the generated UAV path is smoother and converges faster, which can meet the mission requirements.
Object recognition from on-the-road traffic data

In order to detect the object and inspect the road conditions in real-time, the 2-dimensional (2D) and 3- dimensional (3D) data obtained from the onboard sensors, LiDAR and digital cameras are analyzed for object recognition to assist driving. Due to the uncertainties of the dynamic objects, such as pedestrians, animals or vibrated vehicles, extraction of complete and clear objects from LiDARs datasets requires complex post-processing since LiDAR data can be used for scanning at long distances, i.e., 300m, which can alarm the driver timely to take necessary actions. The dynamic and static objects from the LiDARs point clouds can be detected with the teacher-student framework algorithm along with the KITTI dataset. Furthermore, a semi-supervised theory is utilized to improve detection performance.
Confiner Based Video Captioning Model

Video captioning is the interesting task of encoding features and decoding the encoded features into natural language. Video captioning task is the perfect blend of computer vision and Natural language processing techniques. Video captioning process is a tentative mission since it must consider temporal features along with spatial features in order to generate appropriate captions. This task has the default framework as encoder-decoder. Ensuring the optimality of the generated caption for the particular video is the most important and essential thing which is not considered by most of the existing works. In order to ensure the optimality of captions for videos, our proposed work introduces confiner to the default framework. Confiner is planned to diminish the semantic gap among the generated caption and videos. Confiner is designed in our model with both LSTM and GRU separately as two different models. The actual work of the confiner is to inspect the visuals of the generated caption with the actual video. The proposed model has experimented with three different benchmark datasets as MSVD, MSR-VTT, and M-VAD. The performances of the models are measured by BLEU, METEOR, CIDEr evaluation metrics.
Recorded: A Visual and Physical Revivalist Ode to Vinyl Culture

In an ever-evolving digital world, where attention and experimentation allow only an increasingly temporary effect, the rediscovery of physical attachment offers new relief and a degree of technological resistance that can be recognized as a search for the individual and the contemplative. Through the authorial connection to a subculture of exponential dimension the world over, “Recorded” navigates the musical universe of the vinyl record, proposing a physical and graphic determination of its essence. With the involvement of synaesthesia as a central part of the project’s strategic design, the object, a prototype based on a similar concept to a record collector’s edition, unfolds through several applications that promote an immersive interaction between user and components. Developed under the historical and contemporary premise of framing the frenzy that accompanies vinyl, the multiple explains it, through a modulated and adapted book strategically positioned inside it. This, along with the other graphic elements that complement the book, allows interaction with the reader, thus promoting and reshaping the concept of the collector’s edition. The collection is about the pleasure of feeling, cherishing, and protecting.
Intellectual Property Rights (IPR) in the Blockchain Era

The chapter examines the concept of intellectual property right protection in the emerging blockchain era. Libraries may not keep all forms of intellectual property in their custody, but keep information about them. It is therefore important to examine the implication of the emergence of DLT, blockchain, Web 3.0, the Metaverse, and other related technologies to the library and information industry. The progress and well-being of humanity depend on our capacity to come up with new ideas and creations. The importance of information and information technology in the global marketplace is so strong that companies that possess the appropriate knowledge are often more profitable than their competitors. Intellectual property is widely acknowledged today as a key component of economic development.
The evolution of the fAIble system to automatically compose and narrate stories for children

This article describes our long-term research into automated story generation and our resulting story generation architecture called fAIble that incorporates several innovations. fAIble determines each event that occurs in the tale using a combination of scripted sequences and stochastically chosen events. The probability of an event occurring is based on the skills and personalities of the characters who have agency. Event selection is also influenced by the context of the situation faced by the characters. Each event is associated with a description in grammatically-correct natural language that can be narrated orally via text-to-speech. We describe the evolution of fAIble, its architecture and the results of our independent evaluation of each of the four progressively developed fAIble prototypes (fAIble 0, I, II and III), as tested with human test subjects. On a continuous scale where 0 means unacceptable, 1 means acceptable and 2 means optimal, the composite human test subject rating average from the independent tests of the prototypes was 0.933. The paper also describes a summative assessment where test subjects were asked to review stories from all four prototypes and rank them comparatively. These comparative results indicate an improvement from the original (fAIble 0) to the last one (fAIble III).
Semi-structured Patient Data in Electronic Health Record

In recent days, many healthcare organizations adopted Electronic Health Records (EHR) which furnishes many significant features to patients, doctors, and healthcare service providers. The number of healthcare organizations that have adopted EHR is pretty low due to the privacy and security concern of patient information. In order to overcome these issues, EHR should become capable of safeguarding the huge quantity of health records. The current study focuses on the ethical issues with modern technologies, security, and privacy aspects of EHR. The current study also discusses the role of semi-structured data in EHR.
Continual Self-Supervised Domain Adaptation for End-to-End Speaker Diarization

In conventional domain adaptation for speaker diarization, a large collection of annotated conversations from the target domain is required. In this work, we propose a novel continual training scheme for domain adaptation of an end-to-end speaker diarization system, which processes one conversation at a time and benefits from full self-supervision thanks to pseudo-labels. The qualities of our method allow for autonomous adaptation (e.g. of a voice assistant to a new house-hold), while also avoiding permanent storage of possibly sensitive user conversations. We experiment extensively on the 11 domains of the DIHARD III corpus and show the effectiveness of our approach with respect to a pre-trained base-line, achieving a relative 17% performance improvement. We also find that data augmentation and a well-defined target domain are key factors to avoid divergence and to benefit from transfer.