Reviewerly/exHarmony huggingface dataset:
Comparative Study of Cryptographic and Biometric Signatures

The paper compares the two primary methods of electronic signature: (a) cryptographic electronic signature (CES), and b) dynamic biometric signature (DBS) in terms of key features such as: data used for signature, the possibility of counterfeiting, theft or misuse of the signature, time limitations of signature, test if signing person is alive, environmental influences during signing process, verification checks of signature authenticity, security of methods, ability to verify document integrity, necessity of use of special devices, complexity of implementation, ease of deployment in organization, simplicity of use for users, level of general method acceptance, limitations from the point of view of personal data protection and limitations determined in specific legal acts. Based on many practical observations the listed properties of both methods were scored. The results showed that both types of signatures were satisfactory in all of the above mentioned criteria, however, the overall score was higher in case of DBS. The EU legislation currently prefers CES over DBS by placing the signatures based on a qualified certificate within a hierarchy of signatures higher than the signatures created without using these certificates. Despite the fact that the possibility of separating certificates from the signing person can be considered as a major weakness of this method. The strong preference of CES has neither legal nor technological justification because the requirements for signatures in electronic documents are much stricter than for signature on paper. According to the authors, DBS should be considered more as a traditional signing method that is supplemented by indisprovable attributes in the form of a biometric trace unique to each person.
Developing Creative Thinking in Preschool Students by Volshebniki Art Studio

Today, to raise a proactive creative person, it is necessary to create conditions for self-fulfillment and self-development of creative capacities as part of productive activities by changing the established education paradigms associated with the accumulation of already existing knowledge. The authors of this article believe that the goal of today’s preschool education is to create conditions for comprehensive and systematic development of creative thinking in children of all age groups. This article dwells upon the development of creating thinking in senior preschool students. The authors assessed the development level of children's creative thinking using Williams’s divergent (creative) thinking test, Druzhinin’s creativity level identification method, and the adapted method of Shaidurova. The authors developed and tested the Volshebniki ART Studio project that employs the following methods: play therapy, visual art therapy, pedagogue and children's storytelling, color therapy, paper crafting, collage, origami, and fairy tale therapy.
Medical assistant diagnosis method based on graph neural network and attention mechanism

The electronic medical record contains the patient description information, which can help doctors quickly make the initial clinical diagnosis when facing similar patients. Aiming at the problem of clinical intelligent aided diagnosis, a method based on graph neural network and attention mechanism is proposed. The graph neural network is used to model the label space, and then label classifier based on attention mechanism is used to classify. In the label space modeling, combined with the hierarchical structure of international classification of diseases (ICD), the structure information of label space is integrated into the model. The experimental results show that, compared with the traditional text classification modeling method, the proposed method has a significant improvement in classification performance.
A Method to Generate Posed Person Image with few Context Images

We report a method to generate an image of an arbitrary person in an arbitrary pose, where the person&#x2019;s characteristics are provided as a few sample images. The rapid development in image generation techniques using deep neural networks makes it possible to generate photo-realistic images. However, controlling the content of the generated images, such as a person&#x2019;s clothing or pose, is still an open problem. The method we propose creates a pose invariant person representation using the attention mechanism and generates posed images by applying pose query on the representation. We evaluated the method using 3D rendered synthetic data and confirmed that the created person representation is pose-invariant, and we can render good quality images with the representation.
TaxoMulti: Rule-Based Expert System to Customize Product Taxonomies for Multi-Channel E-commerce

Abstract Taxonomies are used as product categories to facilitate users navigating through an e-commerce portal with the help of hierarchically structured concepts. However, the identical taxonomy is shown to each customer regardless of the channel used. This is challenging for the customers in terms of user experience, as the screen size is rigid, and has not a flexible format like a printed catalog. Simply reducing the taxonomy as suggested in existing works is not sufficient, as it leads to semantic misrepresentation of the product domain. To overcome the inflexibility of product taxonomies, the rule-based expert system TaxoMulti is presented in this paper. The main objectives of our descriptive research are the formulation of the taxonomy over- and undersize problem in multi-channel context, before different types of flexible mediator concepts are discussed that allow overcoming these challenges. Using our novel method, marketing experts can now provide different taxonomies including the same semantics to be shown on different channels. The method is implemented using logic programming, allowing the integration of an inference engine utilizing background knowledge without changing the underlying logic of the used information (management) system. The comprehensive experiments on three public and private databases highlight the improvement when adding different types of mediator concepts for the adaption process. Compared to existing best performing works in related fields, TaxoMulti has achieved an improvement of + 26.31 % for the reduction of the taxonomy, + 60 % for the enlargement of the taxonomy, and + 21.21 % in terms of flexibility.
A Machine Learning Framework for Predicting Sports Results Based on Multi-Frame Mining

Machine learning is an intelligent method that plays a good role in the field of classification and prediction. The prediction results need to be prepared in the field of sports prediction. This is because gambling in large-scale sports events involves a lot of money. At the same time, sports professional club managers and bosses are working hard to establish intelligent video processing and resource allocation, SQL as a database. These models involve many factors related to the game, such as historical game results, player performance indicators, and opponent information. Based on the theory of multi-frame mining, this paper develops a machine learning framework for predicting sports results to analyze the results of sports events.
The Effect Of Derivative Product At Periodic Points On Chaotic Ergodic Properties Of A Function

Taking a function of one variable as an example, the relationship between its derivative at a periodic point and its chaotic ergodic property is studied. It is found and proved that if the absolute value of the product of N derivatives at N periodic point is less than 1, then the function cannot be ergodic chaotic. Then, this result is used for image encryption, which further confirms the main conclusion of the paper.
A Comprehensive Study of Cognitive Graphs: Techniques, Applications, and Challenges

The realization of third-generation artificial intelligence (AI) requires the evolution from perceptual intelligence to cognitive intelligence; in this context, knowledge graphs may no longer meet practical needs. Based on dual channel theory, cognitive graphs are established and developed by coordinating an implicit extraction module and an explicit reasoning module, as well as integrating knowledge graphs, cognitive reasoning, and logical expressions, which have achieved successes in multi-hop question answering. The widespread use of cognitive graphs in advanced AI applications (e.g., large-scale knowledge representations and intelligent responses) is desirable, which will promote the development of AI dramatically. This review discusses cognitive graphs systematically and elaborately, including their basic concepts, generation, theories, and technologies. Moreover, we try to predict the development of cognitive intelligence in the short-term future and further encourage more research works and studies.
Machine Learning Classification Methods Informing the Management of Bovine Tuberculosis Incidents in England

Despite decades-long disease control and eradication programmes, bovine tuberculosis (bTB) continues to pose a problem in developed countries like the UK. One of the costliest and most complex animal health problems in England, it affects the cattle industry and the health of other mammals including humans. The effectiveness of the test-and-slaughter policy, cornerstone of the eradication strategy, is hampered by the imperfect sensitivity of the routine diagnostic tests applied.Improving diagnostic testing to root out bTB more effectively, with the additional deployment of more sensitive tests, is one of the top priorities for the government in the current phase of eradication. However, their blanket deployment implies not only an excess of false positives but also logistical and cost-efficiency challenges.We have explored the application of enhanced disease control measures to increase the detection of infected cattle in surveillance tests that detect inconclusive reactors in the absence of reactors, informed by classification machine learning algorithms (classification tree analysis and random forest). Here, we describe their use to direct the application of additional disease control measures in herds at risk of having an incident (i.e. a positive bTB test result) in England in 2020.Both models demonstrated an AUC > 95% and classification tree analysis was chosen due to its easy-to-interpret decision tree output, moderate sensitivity (73%) and very high specificity (97%) in the 2012-2019 test dataset. Eight percent or 3 510 out of a total of 41 493 eligible active herds in 2020 were predicted by the model as at-risk of a bTB incident, with the majority of them experiencing at least one (66% or 2 328 of them).All predicted at-risk herds could be subject to preventive measures and the additional application of existing incident control measures to the ones having an incident could improve the detection of infected cattle. While 636 inconclusive reactors would have additionally been removed as dangerous contacts (330% increase), 8 585 additional IFN-gamma test reactors were detected in high-risk incident herds in England in the year 2020 (217% increase), the blanket application of the IFN-gamma test increasing the number of reactors by 44%, on average.Classification tree analysis ensures the systematic identification and targeting of high-risk herds to consistently apply additional risk-reduction disease control measures. These measures can help preventing incidents and increasing the detection of infected cattle in affected farms, accelerating the decrease in new incidents needed to achieve eradication by 2038.
The Huffman Tree Problem with Upper-Bounded Linear Functions

The construction of a Huffman code can be understood as the problem of finding a full binary tree such that each leaf is associated with a linear function of the depth of the leaf and the sum of the function values is minimized. Fujiwara and Jacobs extended this to a general function and proved the extended problem to be NP-hard. The authors also showed the case where the functions associated with leaves are each non-decreasing and convex is solvable in polynomial time. However, the complexity of the case of non-decreasing non-convex functions remains unknown. In this paper we try to reveal the complexity by considering non-decreasing non-convex functions each of which takes the smaller value of either a linear function or a constant. As a result, we provide a polynomial-time algorithm for two subclasses of such functions.
Value of Random Vector Functional Link Neural Networks in Software Development Effort Estimation

Software development effort estimation (SDEE) plays a key role in major project management activities. However, traditional parametric (algorithmic) SDEE techniques cannot accurately capture the nonlinear relationships between project attributes and project effort or cost. Against this backdrop, we conducted an empirical analysis of a state-of-the-art RVFL-neural network-based SDEE model. This study investigated the efficacy of RVFL neural networks in improving the accuracy of SDEE. RVFL neural networks, as learning tools, have achieved a remarkable performance in various applications due to their unique characteristics. RVFL neural networks are computationally simple, fast, and efficient SLFNs directly linked from the input layer to the output layer. This study adopted the Chebyshev polynomial orthonormal basis function, ridge regression-based closed-form solution with uniform distribution of weights in the range [−1, 1], and sigmoid activation function. In addition, an extensive simulation study was performed on the PROMISE repository test suite to ascertain the efficacy of the proposed RVFL-based SDEE technique.
A Study On Detection Of Fake Commodity Reviews Based On ERNIE-BiLSTM Model

The user’s commodity reviews displayed on ecommerce platforms will affect consumers’ consumption decisions. The detection of fake reviews can provide reference for consumers to screen the review information and protect the legitimate rights and interests of consumers and merchants. In order to improve the detection effect of fake Chinese reviews, this paper proposes a fake review detection model based on ERNIE-BiLSTM, which takes fake review detection as a text classification task, uses ERNIE network layer to generate dynamic word vectors, and uses BiLSTM neural network to extract features. Through the test experiments on the data set of fake reviews in Chinese commodities collected and labeled, the ERNIE-BiLSTM model was compared with the BERT-BiLSTM model in terms of accuracy, recall rate, precision and F1 score. The results show that the ERNIE-BiLSTM model is superior to other models in the detection of fake reviews.
Railroad Segmentation Using Modified UNet

One of the complex activities in our daily life humans do is driving. We humans handle unexpected situations like reacting to weather conditions, making decisions against the rules which make us save human life who are in danger. Thus, in this situation, we are approaching a technique artificial intelligence which is the actively researched area in autonomous driving. To succeed in this field, they will be using hardware and software components that support this function. The components like LIDAR, cameras, sensors and computer vision algorithms are need to be implemented more closely which help us to achieve autonomous driving. Generally, nowadays, in many places, trains lead to autonomous driving by detecting the rail path which means there is no any interaction of humans in driving. To detect the path, we had used encoder and decoder part, where in each part, there consists of few levels, where in each level, we had used CNN layers like convolution layer, pooling layer and convolution transpose layer. We have collected the dataset manually from Web, labeled using Photoshop and masked using MATLAB. We had compared this model with existing methods where the accuracy of our model is 92%, and segmentation is very good in our case. Thus, we have concluded that modified UNet provides better accuracy compared to all other methods.
Design of Automatic Evaluation of Machine English Translation Based on BP Neural Network

In recent years, with the development of deep learning, the automatic evaluation model of neural network machine translation based on deep learning has been widely used. In addition, the task of machine translation quality assessment is closely related to machine translation. The purpose of this article is to study the design of automatic evaluation of machine English translation based on BP neural network. This article proposes a method to extract features containing translation knowledge with the help of a neural network machine translation model. This article evaluates the quality of machine translation from the perspective of language error analysis theory, analyzes the errors and their causes, and helps provide quality support for machine translation research from the perspective of computer science. Experimental studies have shown that as the scale of the data set grows, the accuracy of both BLEU value and n-grams reaches about 99.3, and the model effect is significantly improved.
A SWOT Analysis of Virtual Reality for Seniors

The population of older people is increasing. In this book chapter, the author reviewed some of the applications of virtual reality (VR) for seniors in form of a strengths, weaknesses, opportunities, and threats (SWOT) analysis. This approach is commonly used to assess and guide internal and external parameters to accomplish a mission. Some of the challenges that the elderly are facing were reviewed and the way VR can help in overcoming those challenges were discussed. A comparison of VR technologies was also presented. The results show that VR's feasibility and concept are linked to existing technologies and the characteristics of each system control immersion, interaction, and stimulation. VR interventions might have the potential to be used as ecologically valid e-health screening systems. Systems should be specifically designed and adapted for seniors and usability issues should be addressed. Ideally, VR interventions work best in addition to traditional methods. Future studies should use more subjects to ensure the efficacy of such interventions for seniors.
We Know An Emotion There, But What Type Is It and What Triggers It? Towards Emotion-Cause Triplet Extraction

A recently emerging emotion analysis task is emotion-cause pair extraction (ECPE), which aims to simultaneously obtain emotions and their corresponding causes expressed in documents. While ECPE is appealing, it does not take into account the types of emotions when connecting them with causes. The emotion type can reflect affective states and subjective information being recognized. Without it, an emotion-cause pair is of limited usefulness in practical applications such as strategy formulation, decision making, etc. To address this issue, we propose a new task, named emotion-cause triplet extraction (ECTE). It not only extracts emotion-cause pairs but at the same time also distinguishes emotion types. Further, we establish a biaffine attention-based multi-task learning approach to the ECTE task. Experiments are carried out on a benchmarking emotion-cause corpus that is slightly modified to suit the ECTE task. The results demonstrate the feasibility of the new task and the effectiveness of our proposed approach.
Efficientnet Model Based Gesture Recognition Method Research

Aiming at the problem of high computational complexity and accuracy of dynamic gesture recognition, this paper proposes a multi-feature fusion dynamic gesture recognition based on improved EfficientNet[1] model. Firstly, for each video data set, video frames are cut at a certain FPS and saved as training set and test set, and the classification performance of images is regarded as the classification performance of corresponding videos. Then, feature extraction and model fusion are used to extract dynamic gestures of characters, so as to extract key actions and training classification models. After the training, load the model to check and verify all the video frames in the test set, and get the output of top1 accuracy rate and Top5 accuracy rate in the whole test set. Finally, real-time detection is carried out. The experimental analysis of 170 video files shows that the average accuracy of the improved method is significantly higher than that of other common neural networks. The average detection accuracy in various gestures such as click and zoom was 93%.
Intelligent Recommendation of Legal Articles Based on DPCNN with Capsule Model

Intelligent recommendation of legal articles is recommending relevant legal articles by the description of facts in criminal cases.The technology is fundamental in the field of Intelligent Justice and greatly increases the efficiency of judicial practice. CAIL2018 is the first large-scale Chinese legal dataset, which is very challenging because of unbalanced distribution of samples in different categories. Then, we propose a DPCNN with capsule model for this task, which can effectively improve the accuracy of multi-label classification and achieve good performance. This Model extract low-level features of different length segments of text through DPCNN and capture high level features by capsule, therefore it retains features completely and has good generalization. Moreover, for a higher score, we improve its performance by focal loss, data augmentation and oversampling, which make the model FI score reach 80.3.
Improving Utterance Rewriter Based on MMI and Text Data Augmentation

In multi-round dialogue tasks, how to maintain the consistency of model answers is a major research challenge. Every answer to the model should be time dependent, causal, and logical. In order to maintain the consistency of the personality, dialogue style, and context of the model, it is necessary to retain the key information in the historical dialogue as much as possible so that the model can generate more accurate answers. Utterance rewriting is a technique that replenishes the information of the current sentence by analyzing the historical dialogue, so as to retain the key information. This paper mainly uses text augmentation, Maximum Mutual Information (MMI) method and character correction method based on Knuth–Morria–Pratt (KMP) algorithm to improve the effect of utterance rewriting generation. The number of original statement rewriting datasets is limited, and the cost of manual manufacturing is too high. By using the method of text data augmentation based on coreference resolution, the positive dataset that is missing from the statement rewriting dataset is repaired. At the same time, the existing datasets are expanded to increase the number of data. The generated results are optimized by using the MMI method, and the KMP character correction method is used to modify the wrong characters to improve the overall accuracy.
Tire Pattern Image Retrieval Algorithm Based on Optimized Efficientnet

Tire pattern image has always been an important clue in the handling of traffic accident cases and criminal investigation cases. By analyzing the tire pattern information left at the scene of the accident, the investigators can quickly narrow the scope of case detection, and even lock the vehicle and personnel information. Compared with the existing deep learning networks, EfficientNet uses the method of composite scaling to effectively scale the model to obtain the retrieval accuracy under the optimal structure. In this paper, based on the EfficientNet model, we propose a tire pattern image retrieval algorithm based on optimized EfficientNet. The basic network model EfficientNet-B0 is selected to extract the tire pattern image features, and the cross-entropy loss function is used in the model training, In the model optimization, we use AdamW + Cosine annealing, a variant of Adam Gradient Descent method, to further improve the network performance and the retrieval accuracy.