Reviewerly/exHarmony huggingface dataset:
About the Parsing of NMEA–0183 Format Data Streams in GPS

NMEA-0183 protocol parsing module is the most important software module in navigation, therefore, this paper on the basis of analyzing NMEA 0183 statements, through the C language of the effective parsing and study of the middle field of different statements, wrote its related port communication program, and finally realized NMEA-0183 original data extraction, parsing processing and transmission process, verified by STM32 development board and host computer communication.
Knowledge Representation by Generic Models for Few-Shot Class-Incremental Learning

Few-shot class-incremental learning (FSCIL) means identifying new classes in a few samples while not forgetting the old ones. The challenge of this task is that new class has only few supervised information during the learning process. Aiming to boost the performance of FSCIL, we propose a novel method in this paper. To be clear, our method has two contributions as follows. First, we elegantly employ the principal component analysis (PCA) and adopt a model with a strong prior for feature extracting, specifically, we decouple the feature extractor from classifier in the incremental learning process. Second, we innovatively introduce the data augmentation during the learning process of FSCIL to enhance the sample diversity and get a more accurate class prototype based on enriched samples. Excellent experimental results on CIFAR-100, miniImageNet, and CUB200 datasets verify the superiority of our method, compared to several existing methods.
Model Inversion-Based Incremental Learning

As developments in the field of computer vision continue to be achieved, there is a need for more flexible strategies to cope with the large-scale and dynamic properties of real-world object categorization situations. However, regarding most existing traditional incremental learning methods, they ignore the rich information of the previous tasks embedded in the trained model during the continuous learning process. By innovatively combining model inversion and generative adversarial networks, this paper proposes a model inversion-based generation technique, which makes the information contained in the images generated by the generator more informative. To be specific, the information in the model, which has been trained by the previous task, can be inverted into an image, which can be added to the training process of the generative network. The experimental results show that the proposed method alleviates the catastrophic forgetting problem in incremental learning and outperforms other traditional methods.
Towards Robust Similarity Detection of Smart Contracts with Masked Language Modelling

Smart contracts are programs that run on blockchains. The whole smart contract ecosystem tends to be highly homogeneous, due to the immutable nature of contracts once deployed, as well as the copy-paste practice in developing smart contracts. Thus, similarity detection between smart contracts is of great value, which facilitates the quality assurance of the whole ecosystem, by providing a way to identify and track clones among the smart contracts. To this end, this work presents SoliSim, which encodes smart contracts into informative semantic vectors for effective and efficient similarity detection. The smart contract encoding procedure is enforced with masked language modelling on the Solidity programming language, which pre-trains a bert-like model by feeding in normalized token sequences extracted from the smart contracts’ abstract syntax trees (ASTs); while the similarity detection procedure is enforced via simply calculating a score on the encoded numerical vectors. As the experimental results show, the pre-trained strategy adopted by SoliSim is capable of capturing the contextual and semantic information of smart contracts’ code. The similarity scores calculated with SoliSim on pairs of real cloned contracts all exceed 96%, while the values between non-clone pairs are all below 50%.
Han Dynasty Clothing Image Classification Model Based on KNN-Attention and CNN

Aiming at the problem that the traditional clothing image classification model cannot effectively extract the instance information of other samples in the training set, a clothing image classification model of Han Dynasty based on KNN-Attention and CNN was proposed. Firstly, KNN-Attention was used to extract the clothing image information of K instance samples similar to the original training samples. Secondly, CNN is used to further extract the local key features of clothing images. Finally, the output information of KNN-Attention and CNN is integrated, so as to achieve the purpose of effectively utilizing the training set instance information in the task of clothing image classification. The experimental results show that the proposed model is better than the traditional classification model and can effectively improve the classification effect of clothing images.
Analysis of Test Scores of Insurance Salesman Based on Improved K-means Algorithm

In order to study the problems existing in the training and examination of marketing staff in insurance companies, it is necessary to conduct k-means clustering analysis on the learning status of marketing staff, so as to adopt different training programs for different groups and improve the theoretical level and actual productivity of insurance marketing staff. However, the traditional K-means algorithm has the problems of sensitive selection of initial points and unstable clustering results. This paper proposes an improved K-means algorithm. The algorithm combined the advantages of K-means++ and dichotomized K-means, weakened the dependence of clustering results on the initial clustering center, and evaluated the iterative clustering effect by contour coefficient. The experimental results prove the practicability of the improved K-means algorithm in the analysis of the test scores of insurance salesmen, and the clustering results can provide scientific guidance for the development of training programs of insurance salesmen and the improvement of their academic performance. Moreover, the clustering result of the algorithm is more stable than that of the traditional K-means algorithm, and the clustering effect is better.
Three-Segment Waybill Code Detection and Recognition Algorithm Based on Rotating Frame and YOLOv5

Aiming at the inefficiency of the manual sorting method used in sorting centers at district and county levels, an algorithm is proposed for the detection and recognition of three-segment waybill codes based on rotating frames and YOLOv5. First of all, the detection of rotation angle is achieved by converting the regression problem into a classification problem through a Circular smooth label to solve the influence of angle periodicity on training. Secondly, the improved algorithm is used to detect the waybill Logo, according to the position relationship between the Logo and the three-segment code, and then combine the Logo position and rotation angle to locate the three-segment code, and use Radon transform to correct the tilt of the three-segment code to improve the three-segment code locating accuracy. Finally, the template matching algorithm is used to identify the three-segment codes. The experimental results show that the proposed algorithm achieves 93.6% detection accuracy of courier Logo and more than 99% positioning accuracy of three-segment code, which can effectively improve courier sorting efficiency.
Ease Solidity Smart Contract Compilation through Version Pragma Identification

Solidity, the most popular language for developing smart contracts that run on blockchains, has experienced a fast version changing since its first release. Yet, the fact that different Solidity versions are generally incompatible, makes it torturous for developers to correctly pick the Solidity version to compile a smart contract missing version-indicative information. This work presents SolCom to ease the compilation of a Solidity smart contract. It infers from the source code of a smart contract its version pragma, with which the developers can accurately determine the right-version Solidity compiler to get the source code compiled rather than aimless attempts. SolCom processes the source code into normalized tokens, and resorts to a carefully-designed attention augmented convolutional neural network model to capture version indicative features. As the experimental evaluations show, SolCom can identify the version pragma with a rather high accuracy of 96.30%.
Multi-title Attention Mechanism to Generate High-Quality Images on AttnGAN

In the field of Text-to-image, text is essentially a constraint condition for the generated image, and the generation network guides to generate images that match the text according to the constraint conditions. However, if the image is generated only on the basis of a given text constraint condition, obviously, it can be imagined that the generated image without rich details, reducing the image visualization. With that in mind, we introduce Multi-title Attention Mechanism, regard the dataset as a prior condition, at first, select other titles in the dataset that are compatible with the given text according to given title, which is essentially the process of information retrieval, and then use the self-attention mechanism to integrate the embedding of multiple titles, the final text contains rich detail information, which guides the generation of high-quality images. In addition, in order to enable AttnGAN to generate clear image in the first stage, we introduce a mixed attention mechansim and an Residual Dense Block(RDB) model. The mixed attention mechanism includes: channel attention and pixel attention. Channel attention is mainly to guide what the image is generate, while pixel attention is responsible for where it is generated. Experiments on the CUB dataset show that the proposed approaches is significantly better than AttnGAN, and the lnception Score(IS) and R-precision of the evaluation index are improved by 4.12% and 10.43% respectively.
Global Digital Society and the Problems of Social Adaptation

This paper explores problems of social development in the modern global world, as determined by objective, subjective, economic, political, spiritual, and other factors. The scientific and technological development taking place in the modern world has radically changed the vector of civilizational development as a whole: new relations are emerging in society, leading to new paradigms. This paper provides an analysis of the social and philosophical significance of digitalization, including blogging and banking. The author argues that in these complex social phenomena, one should not look only for the positive; just like in any other large-scale changes, digitalization, blogging and electronic banking have certain disadvantages. The author raises for discussion their negative aspects and the dangers that lurk in them. The author also analyzes the significance and dependence between the development of industrial and humanitarian intellects, and the preservation of the techno-humanitarian balance, and concludes that the higher the potential for production technologies, the more perfect the means of influencing nature and the less aggressive the struggle for the survival of mankind. And this, according to the author, is the basis for maintaining the fragile balance of the anthropo-socio-techno-natural complex.
New Constructions of Sidon Spaces and Cyclic Subspace Codes

Sidon space is an important tool for constructing cyclic subspace codes. In this letter, we construct some Sidon spaces by using primitive elements and the roots of some irreducible polynomials over finite fields. Let q be a prime power, k, m, n be three positive integers and $\rho= \lceil \frac{m}{2k}\rceil-1$, $\theta= \lceil \frac{n}{2m}\rceil-1$. Based on these Sidon spaces and the union of some Sidon spaces, new cyclic subspace codes with size $\frac{3(q^{n}-1)}{q-1}$ and $\frac{\theta\rho q^{k}(q^{n}-1)}{q-1}$ are obtained. The size of these codes is lager compared to the known constructions from [14] and [10].
Bosons

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Environmental Consequences of Mining Bitcoin: The Carbon Emission in China

In recent years, as cryptocurrency has been recognized by more people and the value of the cryptocurrency has increased, many people make money through mining. It leads to mining becoming popular but also creates serious environmental problems. Mining bitcoin will consume much electricity and thus emits more greenhouse gases such as carbon dioxide, which has caused worldwide environmental issues. This paper will focus on figuring out that mining bitcoin will cause how much carbon emission damage in China during these years. The result of this research will provide the change in carbon emission with time and the prediction of the carbon emission trend caused by bitcoin mining in the future. This result of the article aims to focus the society and the government's attention on the damage mining bitcoin does to the environment and also provide suggestions on the measures the governments should take to reduce the unessential energy cost of bitcoin mining.
Application of Nanomaterials for Food Packaging

Nanomaterials have been widely used in a diverse of different fields because of their excellent physicochemical properties, such as food functional materials and food safety testing and analysis. For this reason, nanotechnology in the food business is rising in popularity since it provides numerous new possibilities in this dynamic sector. Applying nanotechnology to the food sector can improve flavor and color, increase barrier characteristics, and boost food safety by detecting bacteria in packaging. In addition, nanofoods have numerous potential advantages, but there are also risks that should be considered. Contents of the piece using a combination of journal and online resources, this article explores the features and benefits of nanotechnology in food packaging, discusses its application in other areas of the food industry, and discusses the potential health risks that food nanotechnology may pose. The article concludes with predictions about the future of nanotechnology, and it is expected to provide a new idea for the application of nanomaterials in the food field.
Exploration of Teaching Mode of Software Project Management Based on Engineering Education Professional Certification

Software Project Management is a core professional course in software engineering major. Combined with engineering education professional certification, we deeply analyze some problems existing in the reform of software project management course teaching mode, and further put forward the teaching improvement strategies. A new teaching model of software project management course is explored based on the analysis of achievement of course teaching objectives, the establishment of all-channel teaching system, and the refinement of assessment and evaluation granularity. The further deepening of the teaching reform of this course can promote the achievement of the training goals of software engineering professionals, and at the same time guarantee the implementation effect of engineering education professional certification.
Domain Adaptation with Maximum Margin Criterion with Application to Network Traffic Classification

A fundamental assumption in machine learning is that training and test samples follow the same distribution. Therefore, for training a machine learning-based network traffic classifier, it is necessary to use samples obtained from the desired network. Collecting enough training data, however, can be challenging in many cases. Domain adaptation allows samples from other networks to be utilized. In order to satisfy the aforementioned assumption, domain adaptation reduces the distance between the distribution of the samples in the desired network and that of the available samples in other networks. However, it is important to note that the applications in two different networks can differ considerably. Taking this into account, in this paper, we present a new domain adaptation method for classifying network traffic. Thus, we use the labeled samples from a network and adapt them to the few labeled samples from the desired network; In other words, we adapt shared applications while preserving the information about non-shared applications. In order to demonstrate the efficacy of our method, we construct five different cross-network datasets using the Brazil dataset. These results indicate the effectiveness of adapting samples between different domains using the proposed method.
Automated Search for Deep Neural Network Inference Partitioning on Embedded FPGA

Deep Neural Networks (DNNs) are currently making their way into a broad range of applications. While until recently they were mainly executed on high-performance computers, they are now also increasingly found in hardware platforms of edge applications. In order to meet the constantly changing demands, deployment of embedded Field Programmable Gate Arrays (FPGAs) is particularly suitable. Despite the tremendous advantage of high flexibility, embedded FPGAs are usually resource-constrained as they require more area than comparable Application-Specific Integrated Circuits (ASICs). Consequently, co-execution of a DNN on multiple platforms with dedicated partitioning is beneficial. Typical systems consist of FPGAs and Graphics Processing Units (GPUs). Combining the advantages of these platforms while keeping the communication overhead low is a promising way to meet the increasing requirements. In this paper, we present an automated approach to efficiently partition DNN inference between an embedded FPGA and a GPU-based central compute platform. Our toolchain focuses on the limited hardware resources available on the embedded FPGA and the link bandwidth required to send intermediate results to the GPU. Thereby, it automatically searches for an optimal partitioning point which maximizes the hardware utilization while ensuring low bus load. For a low-complexity DNN, we are able to identify optimal partitioning points for three different prototyping platforms. On a Xilinx ZCU104, we achieve a 50% reduction of the required link bandwidth between the FPGA and GPU compared to maximizing the number of layers executed on the embedded FPGA, while hardware utilization on the FPGA is only reduced by 7.88% and 6.38%, respectively, depending on the use of DSPs and BRAMs on the FPGA.
Privacy-Preserving Machine Learning in Life Insurance Risk Prediction

The application of machine learning to insurance risk prediction requires learning from sensitive data. This raises multiple ethical and legal issues. One of the most relevant ones is privacy. However, privacy-preserving methods can potentially hinder the predictive potential of machine learning models. In this paper, we present preliminary experiments with life insurance data using two privacy-preserving techniques: discretization and encryption. Our objective with this work is to assess the impact of such privacy preservation techniques in the accuracy of ML models. We instantiate the problem in three general, but plausible Use Cases involving the prediction of insurance claims within a 1-year horizon. Our preliminary experiments suggest that discretization and encryption have negligible impact in the accuracy of ML models.
Interpretable and Reliable Rule Classification Based on Conformal Prediction

This paper deals with the challenging problem of simultaneously integrating interpretablility and reliability into prediction models in machine learning. It proposes to combine the interpretable models of decision rules with the reliable models based on conformal prediction. The result is a new technique of conformal decision rules. Given a test instance, the technique is capable of providing a point prediction, an explanation, and a confidence value for that prediction plus a prediction set. The experiments show when and how conformal decision rules can be used for interpretable and reliable machine learning.
RangeGrad: Explaining Neural Networks by Measuring Uncertainty Through Bound Propagation

When generating local neural network explanations, many methods remove or obfuscate information at the input and observe the effect on the neural network output. If the lack of certain information causes meaningful changes to the output, we assume it was important and forms part of the explanation for the prediction result. It is not trivial, however, to decide on a clear definition for the absence of information. Previous methods have blurred, darkened or added normally distributed noise to certain portions of the input. In this paper, we propose using interval bounds as a proxy for uncertainty about, or absence of, information. Using this insight, we developed RangeGrad, a novel method for generating saliency maps for neural networks. This method exploits the relationship between uncertainty on the input with prediction uncertainty. We show that the uncertainty framework produces valid explanations in line with existing methods.
Rules, Subgroups and Redescriptions as Features in Classification Tasks

We evaluate the suitability of using supervised and unsupervised rules, subgroups and redescriptions as new features and meaningful, interpretable representations for classification tasks. Although using supervised rules as features is known to allow increase in performance of classification algorithms, advantages of using unsupervised rules, subgroups, redescriptions and in particular their synergy with rules are still largely unexplored for classification tasks. To research this topic, we developed a fully automated framework for feature construction, selection and testing called DAFNE – Descriptive Automated Feature Construction and Evaluation. As with other available tools for rule-based feature construction, DAFNE provides fully interpretable features with in-depth knowledge about the studied domain problem. The performed results show that DAFNE is capable of producing provably useful features that increase overall predictive performance of different classification algorithms on a set of different classification datasets.
Anomaly Detection for Physical Threat Intelligence

Anomaly detection is a machine learning task that has been investigated within diverse research areas and application domains. In this paper, we performed anomaly detection for Physical Threat Intelligence. Specifically, we performed anomaly detection for air pollution and public transport traffic analysis for the city of Oslo, Norway. To this aim, the state-of-the-art method SparkGHSOM was considered to learn predictive models for normal (i.e. regular) scenarios of air quality and traffic jams in a distributed fashion. Furthermore, we extended the main algorithm to make the detected anomalies explainable through an instance-based feature ranking approach. The results showed that SparkGHSOM is able to detect anomalies for both the real applications considered in this study, despite the fact it was designed for different tasks.
Frequent Generalized Subgraph Mining via Graph Edit Distances

In this work, we propose a method for computing generalized frequent subgraph patterns which is based on the graph edit distance. Graph data is often equipped with semantic information in form of an ontology, for example when dealing with linked data or knowledge graphs. Previous work suggests to exploit this semantic information in order to compute frequent generalized patterns, i.e. patterns for which the total frequency of all more specific patterns exceeds the frequency threshold. However, the problem of computing the frequency of a generalized pattern has not yet been fully addressed.