Reviewerly/exHarmony huggingface dataset:
High-Performance 300mm Integrated Superconducting Resonators for Quantum Computing Applications

We report on the electrical performance of integrated superconducting microwave resonators at millikelvin temperatures. The resonators were successfully integrated into a 300mm process to achieve tight material and process control. By careful substrate preparation, optimized growth conditions, patterning and postprocessing, we demonstrate record-high resonator quality factors, with a tight control of the resonance frequencies. We have successfully tested several ultra-low loss resonator materials based on elemental and compound superconductors and benchmarked their electrical performance. This work paves the way towards a robust development platform for superconducting qubit technologies.
Teaching analysis research based on mobile learning

With the development of Internet technology and the wide application of multimedia intelligent technology in teaching, mobile learning mode has become a big development trend. This paper describes the theoretical basis of mobile learning, and comprehensively analyzes the deficiencies of the current “online mobile learning” teaching mode in colleges and universities, and puts forward solutions to these problems. On this basis, the mobile learning English teaching mode is constructed in colleges and universities, which gives full play to the advantages of the new teaching mode. The new English teaching model has greatly improved students' self-study ability, fully utilized Internet resources, expanded the student's foreign language learning resource library, and can use the Internet to build an authentic speaking and listening learning environment. The new English teaching model makes full use of the advantages of mobile resources to enrich college English classrooms.
Research on Construction and Application Methods of Semantic Library in the Field of Quality and Reliability

Semantic recognition is the basic link in the field of cognitive intelligence in artificial intelligence, and the semantic library is the bridge of human-computer interaction. Aiming at the problems of scattered knowledge in the field of quality and reliability, diverse expressions, insufficient sharing and reuse capabilities, etc., research has been carried out on the design, construction and application of semantic libraries in the field of quality and reliability, and a domain semantic library management system has been developed to realize the creation and management of domain dictionaries, domain corpus and domain rules. Taking the process quality optimization scenario as an example, the application of semantic library-based scene knowledge graph construction, knowledge reasoning, knowledge search and knowledge question & answer is discussed, and the effective mining, management and sharing of multi-source heterogeneous knowledge in the field of quality and reliability is realized.
Trust Anchor: Zero Trust Architecture for Digital Payments

Digital currencies are under serious consideration but there are many concerns: reliable payment delivery, crime prevention, and safety. This paper develops a Trust Anchor architecture that addresses these concerns through the use of digital signature and blockchain database technologies.
Study on Automated Change Impact Domain Analysis in Regression Testing

This paper presents an automated change impact domain analysis method after software change in software regression testing. Starting from the code change section, the method views the call relation graph related to the change code, finds out all change impact links, locates to functions, components and software, and generates a list of impact files of all change objects. The method is helpful for testing personnel to automate the analysis of change impact domain of the change code on the entire software when performing regression testing, so as to make the testing more sufficient and improve the testing effectiveness and efficiency.
Information Processing Methods and LOT Cloud Platform for Fusion of 5G Urban Component Sensors

With the rapid development of intelligent applications, the development of the Internet of Things changes with each passing day. The Internet of Things, as an essential component of the new information technology, has already become another revolutionary information industry after computers and modern communications. Although the Internet of Things has developed and grown, at the application level, the applications of Internet of Things devices of different architectures are relatively independent of their data information. This paper is based on the research of 5G urban component sensor information processing method and LOT cloud platform, and proposes a data fusion algorithm based on the data sequence of the same time. It analyzes the system process and architecture diagram of the LOT cloud platform, examines the performance of the Hadoop platform on different scale data sets, and uses the stand-alone system as a reference. The experimental results show that the time increase of the cluster decreases with the increase of the amount of data, which shows that the system exerts the advantages of parallel computing under the massive amount of data, and as the data source file increases, the accuracy rate decreases. When the data source size is 2000 MB, the Hadoop cluster system with 3 and 4 cluster nodes has the highest accuracy, 79.21% and 76.28%, respectively.
Applications of Deep Learning Models within National Project IT Academy – Education for 21st Century

A growing interest in promoting and supporting the use of artificial intelligence systems is observed in all major market sectors worldwide, as well as in the public sector. The development of artificial intelligence systems in Slovakia is an important key for innovations in the field of healthcare, digitalization of industry, agriculture, but also other sectors of the Slovak economy. Extending the implementation of activities within the National Project IT Academy – Education for 21st Century by the area of Artificial Intelligence was important in order to provide the educational and training possibilities for university students, to innovate their preparation for employment in the IT sector and to improve their position regarding the information society. In this paper, we present the position of artificial intelligence within the National Project IT Academy – Education for 21st Century. We introduce the basic description of deep learning methods and their applications. Moreover, we present our two educational case studies regarding the applications of deep learning methods in the field of human activity recognition and in the segmentation of electrocardiogram recordings, which represent the examples of outputs of National Project IT Academy – Education for 21st Century.
Solving Shaded Area Problems by Constructing Equations

This paper presents a method for solving shaded area problems that can generate the readable solution because it can adopt the way of constructing equations. Solving shaded area problems is a challenge research problem because of the expression diversity of the problems and the complicated relation between the shaded area and the other areas. Due to the diversity of expression of the problems, it is hard to design algorithms for this research problem. Some efforts have been made on this research problem; however, the proposed methods have some demerits. This paper proposes a novel way, whose main idea lies in constructing equations. Its main step is to acquire a system of equations. A batch of methods is proposed to construct the equations from the inputs. Thanks to the method of constructing equations, the proposed algorithm is concise and the solving process is understandable. The experimental results show that the proposed method is more accurate than the baseline one.
A blockchain-based method and system for trading technological achievements (take the Chinese market as an example)

With the development of a new generation of information and network technology, the number and types of patent infringements continue to increase, and the types of patented products are also more extensive, which hinders the certification and confirmation of patented products, authorized transactions, and patent evaluation. However, the traditional centralized information system is difficult to meet the demand. Blockchain technology provides a shared ledger mechanism and lays the foundation for the construction of credible distributed transaction management. Therefore, this platform is based on blockchain technology to create a safe, stable, transparent and efficient patent service system to provide customers with accurate, objective and efficient quality services.
An Implementation Method Using Cut-Off Bits for Restricted Boltzmann Machines Without Random Number Generators

This study proposes an implementation method of a hardware-oriented restricted Boltzmann machine (RBM) without random number generators (RNGs) that employ cut-off bits, which are obtained from fixed-point binary arithmetic operations on digital hardware, such as field-programmable gate arrays (FPGAs), instead of random numbers. Most FPGA circuits employ fixed-point binary arithmetic operations to improve hardware resource efficiency. Therefore, the proposed method applies the unique feature of the operation, which is bit width extension and cut-off bits. Stochastic neural networks, including RBMs, employ sampling processes based on a probability distribution associated with the network, and the processes require many random numbers. However, implementing RNGs in hardware is costly because it requires considerable hardware resources. The proposed method can mitigate this requirement. To validate the proposed method, we implement an RBM with the proposed method on the software, emulate fixed-point binary arithmetic operations, and train the RBM using the MNIST and Fashion MNIST datasets. Furthermore, we apply the chi-square goodness-of-fit test to evaluate the uniformity of the cut-off bits. Additionally, we compare hardware resource requirements and power consumption for the proposed method and some major RNGs, a linear feedback shift register (LFSR), and a xorshift. Experimental results showed that it was possible to use the cut-off bits for training the RBM using the datasets and clarified the properties of the cut-off bits using statistical analyses. Moreover, hardware implementation of the proposed method involved the lowest hardware resource requirements and power consumption among the RNGs compared in this study.
A Fixed-Point Iterative Method for Discrete Tomography Reconstruction Based on Intelligent Optimization

Discrete Tomography (DT) is a technology that uses image projection to reconstruct images.Its reconstruction problem, especially the binary image (0-1 matrix) has attracted strong attention.In this study, a fixed point iterative method of integer programming based on intelligent optimization is proposed to optimize the reconstructed model.The solution process can be divided into two procedures.First, the DT problem is reformulated into a polyhedron judgment problem based on lattice basis reduction.Second, the fixed-point iterative method of Dang and Ye is used to judge whether an integer point exists in the polyhedron of the previous program.All the programs involved in this study are written in MATLAB.The final experimental data show that this method is obviously better than the branch and bound method in terms of computational efficiency, especially in the case of high dimension.The branch and bound method requires more branch operations and takes a long time.It also needs to store a large number of leaf node boundaries and the corresponding consumption matrix, which occupies a large memory space.
A New Data Hiding Scheme Combining Genetic Algorithm and Artificial Neural Network

Today, in the time of internet based communication, steganography is an important approach. In this approach, secret information is embedded in a cover medium with minimum distortion of it. Here, a video steganography scheme is developed in frequency domain category. Frequency domain is more effective than spatial domain due to variation data insertion domain. To change actual domain of entropy pixels of the video frames, uniform crossover of Genetic Algorithm (GA) is used. Then for data insertion in video frames, single layer perceptron of Artificial Neural Network is used. This particular concept of information security is attractive due to its high security during wireless communication. The effectiveness of the proposed technique is analyzed with the parameters PSNR (Peak Signal to Noise Ratio), IF and Payload (bpb).
Solving Smallest Enclosing Circle Problem by Wading across Stream Algorithm

Abstract The feature of the smallest enclosing circle problem is analysed. According to idea of wading across the stream by feeling the way, a kind of fast efficient wading across stream optimization algorithm is put forward. For solving smallest enclosing circle problem, the improved wading across stream algorithm shrink the search space gradually. It is proved that wading across stream algorithm is a simple and effective algorithm.
Random Sampling of the Zipf-Mandelbrot Distribution as a Representation of Vocabulary Growth by Martin Tunnicliffe and Gordon Hunter

We develop a discrete model of type-token dynamics based on random type selection from the Zipf-Mandelbrot probability distribution, with a view to examining the relationships between the constants of Zipf’s and Heaps’ laws. One model variant assumes an unlimited vocabulary, while the other has an artificially imposed vocabulary limit. We adjust the parameters of both model versions to obtain optimal agreement with the vocabulary growth curves of randomly selected items from the Standardised Project Gutenberg Corpus (SPGC) and compare these optimised parameters with the corresponding values measured independently from the corpus texts.
Synthesis of a Neuro-Fuzzy Model in the Structure of an Automatic Rolling Stock Control System

The problem of constructing a reference kinematic trajectory (RKT) of the movement of rolling stock on the example of the movement of a train is considered. The generated RKT is used in the circuit for regulating the travel time of the automatic control system when calculating the optimal control actions. The structure of a fuzzy neural network (FNN) is proposed, which includes two ANFIS networks that simulate the dependence of the values of the parameters of the optimal trajectory-the stabilization speed and the coordinate of the transition to the run-out during the movement along the stage on the specified travel time and mass of the train. The network training is based on the results of numerical solutions of the optimization problem of train driving modes. A numerical experiment was performed to compare the results of train motion modeling based on the RKT obtained by numerical methods and with the FNN data. Its results confirm the effectiveness of the use of this fuzzy neural network in the optimization of train traffic control in real-time. The simulation was performed in the MATLAB environment.
Training Quantized Deep Neural Networks via Cooperative Coevolution

This work considers a challenging Deep Neural Network (DNN) quantization task that seeks to train quantized DNNs without involving any full-precision operations. Most previous quantization approaches are not applicable to this task since they rely on full-precision gradients to update network weights. To fill this gap, in this work we advocate using Evolutionary Algorithms (EAs) to search for the optimal low-bits weights of DNNs. To efficiently solve the induced large-scale discrete problem, we propose a novel EA based on cooperative coevolution that repeatedly groups the network weights based on the confidence in their values and focuses on optimizing the ones with the least confidence. To the best of our knowledge, this is the first work that applies EAs to train quantized DNNs. Experiments show that our approach surpasses previous quantization approaches and can train a 4-bit ResNet-20 on the Cifar-10 dataset with the same test accuracy as its full-precision counterpart.
A Resolution Enhancement Plug-In for Deformable Registration of Medical Images

Image registration is a fundamental task for medical imaging. Resampling of the intensity values is required during registration and better spatial resolution with finer and sharper structures can improve the resampling performance and hence the registration accuracy. Super-resolution (SR) is an algorithmic technique targeting at spatial resolution enhancement which can achieve an image resolution beyond the hardware limitation. In this work, we consider SR as a preprocessing technique and present a CNN-based lightweight resolution enhancement module (REM) which can be easily plugged into the registration network in a cascaded manner. Different residual schemes and network configurations of REM are investigated to obtain an effective architecture design. Besides, an auxiliary loss is introduced into the cascaded network to empower multi-hierarchical supervision and strengthen the fidelity of the output. In fact, REM is not confined to image registration, it can also be straightforwardly integrated into other vision tasks for enhanced resolution. In the experiments, REM and the cascaded registration network are evaluated on Brain MR images quantitatively and qualitatively at different upscaling factors. It is shown that REM not only improves the registration accuracy, especially when the input images suffer from degraded spatial resolution, but also generates resolution enhanced images which can be exploited for successive diagnosis.
Robust Multi-label Feature Selection via Low-dimensional Embedding and Manifold Regularization

Data dimension is one of the important factors that affect the efficiency of multi-label learning. In order to reduce the dimension of multilabel data, feature selection technology has been widely used in multi-label learning to obtain the most discriminant feature subset. In this paper, a robust multilabel feature selection based on low-dimensional embedding and manifold learning is proposed and named REMFS. Firstly, a low-dimensional embedding is constructed to mine the relationship between feature space and label space. And then we make the low-dimensional embedding and ground-truth label distribution as consistent as possible. In addition, manifold learning is introduced to preserve local geometry for features and labels simultaneously. Then, an iterative optimization scheme is adopted to solve the objective function of REMFS. Finally, comparison experiments are carried out on multiple datasets to prove the effectiveness of REMFS method.
Extended Reality and the Future of Digital Advertising

This chapter introduces the emergence of augmented reality (AR) advertisements, application of AR in business, and the effectiveness of AR in storytelling; virtual reality (VR) advertisements, other mixed reality advertisements; and then discusses recent advancements as well as the future of advertising with mixed reality (AR, VR, MR).
Optimal Weighted Logarithmic Transformation Converted HMOG Features for Automatic Smart Phone Authentication

This paper intends to develop an automatic behavior-based smart phone authentication model by three major phases: feature extraction, weighted logarithmic transformation, and classification. Initially, from the data related to the touches/gesture of the smartphone user, hand movement, orientation, and grasp (HMOG), features are extracted with the aid of grasp resistance and grasp stability. These extracted features are mapped within the particular range by normalizing HMOG. These normalized data are multiplied with the weights followed by logarithmic transformation in the weighted logarithmic transformation phase. As a novelty, the decision-making process related to the logarithmic and weight selection is based on the improved optimization algorithm, called modified threshold-based whale optimization algorithm (MT-WOA). The final feature vectors are fed to DBN for recognizing the authorized users. Finally, a performance-based evaluation is performed between the MT-WOA+DBN and the existing models in terms of various relevant performance measures.
Semantic Interoperability Framework for Digital Finance Applications

Abstract This chapter outlines the theoretical foundation for the design and implementation of the Semantic Interoperability Framework of the INFINITECH project. It details a methodology for semantic models and ontology engineering and prototyping that defines the overall strategy used to design and specify semantic models for digital finance applications. The semantic models are organized hierarchically according to the domain and the specific application and linked to reference ontologies such as the Financial Industry Business Ontology (FIBO), the Financial Instrument Global Identifier (FIGI), and the Legal Knowledge Interchange Format (LKIF). The provided models establish the cornerstone for semantic interoperability within INFINITECH applications while enabling the distributed processing of semantically linked streams.
Machine Learning and Shallow Groundwater Chemistry to Identify Geothermal Prospects in the Great Basin, USA

This study discovers various geothermal prospects in the Great Basin, USA based on shallow groundwater chemical (geochemical) data. The geochemical data are expected to include hidden (latent) information that is a proxy for geothermal prospectivity. We processed the sparse geochemical data in the Great Basin at 14,341 locations for 18 attributes. Next, a non-negative matrix factorization with customized k-means clustering (NMFk) is applied to the geochemical data matrix. NMFk is one of the codes within the SmartTensors framework. NMFk automatically finds three hidden geothermal signatures representing modestly, moderately, and highly confident geothermal prospects. The algorithm also evaluated the probability of occurrence of these types of resources through the studied region. There is a consistency between regional geothermal prospectivities as estimated by our ML methodology and the traditional play fairway analysis conducted over a portion of the study area. We also identify the dominant data attributes associated with each signature. Finally, our ML analyses allow us to reconstruct attributes from sparse into continuous over the study domain. The predicted continuous attributes can be used for future detailed geothermal explorations in the Great Basin.
Attention Network with PMI Value for Medication Recommendation Algorithm

Medication recommendation plays an essential role to help doctors make diagnose in real clinic events more efficient with more and more EHR data in our daily life which contains rich information. Given the patients&#x0027; medical data such as diagnosis, procedure and medication, we propose a novel model APVAN to utilize the medicine background knowledge and the patients&#x0027; historical records in this work. Point-wise Mutual Information value is introduced to construct a medicine graph to illustrate the co-relationship of each medicine. Attention mechanism is adopted to design a new query method to combine different kinds of medical data. Raw data will be embedded first and then use the RNN module to temporally encoding; next, the medicine graph and historical module are combined with a novel query module to make a prediction. A little trick multi-label margin loss is used to solve the label imbalance between the positive and negative samples. Through the experiments, our method achieves the best performance 0.6879, 0.6072, 0.4483 on metrics including, PR-AUC, F1 score and Jaccard.