Reviewerly/exHarmony huggingface dataset:
Improvement and Handling of the Segmentation Model with an Inflation Term

Abstract The use of balloon models to address the problems of “snakes” based models was introduced by Laurent D. Cohen. This paper presents a geodesic active contours model with a modified external force term that includes a balloon model. This balloon model makes the segmentation surface to behave like a balloon inflated by the external forces. In this paper, we show an automatic way to control the behaviour of the external force with respect to the segmentation evolution. The external forces, comprised of edge and inflation terms, push the segmentation surface to edges, while curvature regularizes the evolution. As segmentation evolves, the influence of the applied inflation force is determined by how close we are to the edges. With this setup, the initial segmentation does not need to be close to the object’s edges, instead it is inflated by the balloon model towards the edges. Closer to the edges, the influence of the inflation force is adjusted accordingly. The force’s influence is completely turned off when the evolution is stable (reached the edges), then only the curvature and edge information is used to evolve the segmentation. This approach solves the issues associated with inclusion of balloon model. These issues are that the inflation force can overpower forces from weak edges, or they can cause the contour to be slightly larger than the actual minima. We present examples of the improved model for segmentation of human bladder images. Weak edges are more prevalent in medical images, and the automated handling of the inflation forces gives promising results for this kind of images.
Representative Multi-Domain Feature Selection Based Cross-Domain Few-Shot Classification

Typical few-shot learning methods implicitly assume that the meta-training dataset and the meta-test dataset come from the same domain, which greatly limits the application of few-shot learning methods. To deal with this limitation, cross-domain few-shot classification has been proposed, in which there is a significant difference between the meta-training set as the source domain and the meta-test set as the target domain. To address this problem, we introduce the idea of multi-domain feature selection and propose representative multi-domain feature selection (RMFS) algorithm, which optimizes the multi-domain feature extraction stage and the multi-domain feature selection stage. The effectiveness of the proposed algorithm is demonstrated by experiments on the benchmark dataset Meta-Dataset.
CELA: An Accurate Learned Cardinality Estimator with Strong Generalization Ability and Dimensional Adaptability

Accurate cardinality estimation contributes significantly to query optimization whereas traditional approaches such as histogram-based or sketching-based approaches relying on assumption of uniform distribution of data and appropriate pre-set parameters, often leading to dilemma in practical applications. In this paper, an accurate lightweight, dimensionally adaptive, strongly generalizable learned cardinality estimator for multi-dimensional range queries, CELA is proposed reflecting on the characteristics of desirable cardinality estimators. For the purpose of capturing relationship between dimensions, CELA raises a query-oriented approach of constructing constraint matrices to apply convolution. Experiments illustrates that CELA performs superbly on each defined indicators far superior to PostgreSQL. Furthermore, the strong generalization ability of CELA is demonstrated by the excellent performance trained with continuously scaled-down training set.
Exploring the Impact of Information Security Climate and Information Security Training on Cybersecurity Behavior: Based on Protection Motivation Theory

It has been widely agreed that employees' cybersecurity behavior is a key factor to prevent information security events. According to Protection Motivation Theory (PMT), this study developed a theoretical model to better understand how organization environments encourage employees' cybersecurity behavior. With a survey data of 513 Chinese civil servants, this study empirically found that both information security climate and information security training are positively associated with employees' perceived severity, perceived vulnerability and response efficacy. Furthermore, all the three factors are significantly related to employees' cybersecurity behavior.
Cloud Image Super-Resolution Based on Residual Network

Using cloud image to make weather forecast is the main method for meteorological research in recent years. However the cloud images obtained from current equipment have the problem of low resolution and high noise. To overcome these problems and get a high resolution result, we proposed a cloud image super-resolution method based on residual convolution network. The method can automatically learning the appropriate upscale parameters and merging parameters, which can avoid artificial impacting. In addition to that, the residual structure can help us to avoid gradient disappeared problem. In this method, the feature learning procedure and the reconstruction method was proposed. Experiments show that this method can effectively improve the accuracy of the reconstructed image in detail.
Multidomain Supervised Aspect-based Sentiment Analysis using CNN_Bidirectional LSTM model

Sentiment analysis or opinion mining used to capture the community’s attitude who have experienced the specific service/product. Sentiment analysis usually concentrates to classify the opinion of whole document or sentence. However, in most comments, users often express their opinions on different aspects of the mentioned entity rather than express general sentiments on entire document. In this case, using aspect-based sentiment analysis (ABSA) is a solution. ABSA emphases on extracting and synthesizing sentiments on particular aspects of entities in opinion text. The previous studies have difficulty working with aspect extraction and sentiment polarity classification in multiple domains of review. We offer an innovative deep learning approach with the integrated construction of bidirectional Long Short Term Memory (BiLSTM) and Convolutional Neural Network (CNN) for multidomain ABSA in this article. Our system finished the following tasks: domain classification, aspect extraction and opinion determination of aspect in the document. Besides applying GloVe word embedding for input sentences from mixed Laptop_Restaurant domain of the SemEval 2016 dataset, we also use the additional layer of POS to pick out the word morphological attributes before feeding to the CNN_BiLSTM architecture to enhance the flexibility and precision of our suggested model. Through experiment, we found that our proposed model has performed the above mentioned tasks of domain classification, aspect and sentiment extraction concurrently on a mixed domain dataset and achieved the positive results compared to previous models that were performed only on separated domain dataset.
Emerging Trends in Blockchain Technology to Follow in 2022

Blackchain continues to be in the talk since last few years before and after the pandemic hit. These years are witnessing significant developments in the blockchain technology and its applications and usage in many sectors and industries. Other cryptocurrencies and Bitcoin are gathering attention of the investors but at the same time blockchain too are gaining the focus and attraction. This study tries to trace various key emerging trends in blockchain technology in recent years.
PolyAQG Framework: Auto-generating assessment questions

Designing and setting assessment questions for examinations is always a necessary task for educators. In this article, we identify the research gaps in (semi-) automatically generating questions by evaluating all the available approaches developed thus far. We then propose a framework that puts together previous approaches and suggests ways to fill in their gaps. One hundred and thirteen pieces of literature relevant to question generation approaches have been reviewed and compared. For each of the approaches, the uniqueness of the techniques is explained. The PolyAQG Framework is presented with an explanation of how it would contribute to the solution of the problem, by improving the variety of the questions, increasing the total number of possible choices of question selections, as well as providing a better quality of questions. Apart from the framework, another novelty in this work is the innovative way a domain ontology is used to generate a wider variety of questions.
Influence and Application of Digital Image Processing Technology on Oil Painting Creation in the Era of Big Data

With the rapid development of science and technology, more and more science and technology appear in ordinary life. The auxiliary application of image and digital technology in art creation has become a common phenomenon. With the promotion of photography technology and digital technology, from the realization of visualization of digital technology to now, Image art gives digital technology expression tension, communication effect and market demand. Digital technology has also become a platform, innovative tool and communication carrier of image art. This paper takes the era of big data as the background, combined with the application of cloud computing technology and new technology as the means, This paper discusses the influence and application of digital image processing technology on oil painting creation. Through research and investigation, it is found that 25% of the graduates of Central Academy of fine arts choose to go abroad for further study after graduation, and more than 50% choose to start their own businesses or take part in employment. This is not good news for Chinese oil painting creation under digital image processing technology.
FMR Low-light Tomato Image Enhancement Algorithm Based on Optimized Homomorphic Filter

The problem of low illumination is usually caused by insufficient illumination and backlight. The traditional Frankle_Mccann_Retinex algorithm is easy to cause insufficient edge sharpening and color distortion when processing low illumination images, resulting in dark post-processing images and no effective improvement of details in high brightness areas. To solve this problem, an FMR low-light tomato image enhancement algorithm based on optimized homomorphic filtering is proposed in this paper. Combined with color constancy principle, FMR algorithm of optimized homomorphic filtering is used to process low illumination image, and image compression theory and adaptive median filtering are combined to improve the image. Simulation results show that compared with the classical algorithm, the improved FMR algorithm can effectively improve the brightness of low illumination image and enhance the brightness details of the image, and achieve satisfactory results.
Quantum Key Distribution: An Ascendable Mechanism for Cryptographic Key Distribution

Quantum key distribution (QKD) is a mechanism that works on quantum mechanics principles, which allows communicating users to protect their key exchange from the prying eyes of an eavesdropper. Distributing cryptographic keys among the users and guaranteeing the secrecy of these keys are major concern in cryptography. The protocols of classical key distribution are mainly depend on complexity in computation to decode the key and also on the time required to decode it. Venerable RSA algorithm based public key distribution protocol would be broken either with advancements in high computing hardware and algorithms or with the realisation of the quantum based computers with sufficient number of qubits. Whereas QKD applies the phenomena of quantum physics to ensure secure key distribution. In this paper relevant quantum mechanics principles are briefly described before explaining popular QKD protocols followed by security hacks, available commercial QKD systems & future directions.
Multi-Constraint Particle Swarm Optimization Algorithm in Diet Recommendation for Indian Elderly Persons

A meal recommendation plan should include varieties of food from different categories, and a proper balance of all the nutrients in a required amount. This is a combinatorial optimization problem and is NP-hard. To address NP-hard optimization problems, metaheuristic algorithms having good exploration and exploitation properties are used. This paper proposes a Multi Constraint Particle Swarm Optimization algorithm for meal recommendations for Indian elderly persons. The method adapted easily works with traditional PSO without extra computation cost. It evaluates fitness function based on various nutrient constraints from different categories. Moreover, to improve the particles’ searching efficiency and to improve the quality of the solution, initialization of position and velocity of particles are done randomly from a customized set that includes personalised data obtained during registration. Experiments at an initial level were also conducted, showing positive results.
The Network Education Platform of University Based on Cloud Computing

To improve the awareness of vocational college staff on the informatization of vocational education, take the concept of cloud as the guide, build ideas and implementation methods of digital teaching resources cloud computing platform, enhance the consciousness of vocational college teachers to carry out informatization teaching, improve the level of informatization teaching resources platform construction, and provide theoretical, technical, and practical guidance for vocational college teachers to carry out informatization teaching. Promote the teachers of vocational colleges to improve the ability of information-based teaching design and implementation.
Improving the efficiency of the motion determining of video objects used the contours of their binary images

The method of interframe coding of TV images is discussed in the article. It bases on the edge detection of video objects from their binary images on bit planes of pixel values. Such method of motion estimation allows us more efficiently encode video scenes with a high speed of movement of objects in real time.
Enhancing Class-semantics Features' Locating Performance for Temporal Action Localization

Temporal action localization is a fundamental video understanding task. Meanwhile, due to the complex video background, the varied duration and amplitude of the actions, it is also a considerable challenge. Currently, offline class-semantics representation is the mainstream input of this task since untrimmed videos occupy a large memory, high-quality untrimmed videos and annotations are difficult to access. Because these representations only focus on the class-semantics information, they are sub-optimal for the temporal action localization tasks. At the same time, the exploration of localization-semantics representation is very few due to the high resource consumption. Therefore, it is necessary to improve the detection capability of class-semantics representation directly. As an exploration, we propose the ForeBack module to enhance class-semantics features’ locating performance by augmenting the distinction modeling between foreground and background clips. This module could also eliminate part of the noise of inference probability sequences. Furthermore, we use phased training to learn and use the ForeBack module more effectively. Finally, we reveal the effectiveness of our approach by conduct experiments on THUMOS-14 and the mAP at tIoU@0.5 is improved from 38.8% (BMN action detection baseline) to 47.1%.
The Family Health Strategy and Its Relevance to Geriatric Immunisation Campaigns Against COVID-19 in Brazil

The pandemic caused by the emergence of the new coronavirus disease 2019 (COVID-19) at the beginning of 2020 has set a new reality for public health systems, especially in terms of health care for older adults. In Brazil, there was a need to reinforce actions conceived within primary health care, precisely because it is the ideal field for the development of local, state and national immunisation campaigns against COVID-19. The geriatric immunisation campaign against COVID-19 was carried out through the Family Health Strategy, precisely because it is the preferred model of care. In a few months, a reduction in morbidity and mortality rates was observed in all Brazilian municipalities, which reinforced the importance of this model of management and health care for older persons.
DSP Assembler Auto-Generation Technique and ToolChain Integration

To promote the application of DSP, a fast and accurate toolchain generation method must be realized. Unlike the traditional manual method, in this paper, we propose a toolchain generation algorithm based on the architecture description language, and migrate the toolchain to adapt to the specified processor architecture. The algorithm uses the DSP architecture description as input to generate intermediate expressions, then uses lexical analysis and syntax analysis tools to migrate the assembly toolchain. In addition, an integrated development environment is developed based on Eclipse, which creates a friendly human-computer interface. Experiments show that this toolchain generation algorithm runs fast and accurately, which solves problems of the development and application of the DSP toolchain, and creates favorable conditions for the promotion of DSP.
Roman Manipular Warfare as a Mega-Weapon of Antiquity

We present a coherent picture of the functioning of a Roman infantry legion based on a systematic procedure of finding weak points in the enemy formation as a destination for persistent attacks.We conjecture that this tactic was a robust and resource-efficient way to victory.Its discovery could be one of the primary reasons for developing a manipular legion, a unique way of fighting that shaped Roman warfare.
Detecting Adversarial Samples with Graph-Guided Testing

Deep Neural Networks (DNN) are known to be vulnerable to adversarial samples, the detection of which is crucial for the wide application of these DNN models. Recently, a number of deep testing methods in software engineering were proposed to find the vulnerability of DNN systems, and one of them, i.e., Model Mutation Testing (MMT), was used to successfully detect various adversarial samples generated by different kinds of adversarial attacks. However, the mutated models in MMT are always huge in number (e.g., over 100 models) and lack diversity (e.g., can be easily circumvented by high-confidence adversarial samples), which makes it less efficient in real applications and less effective in detecting high-confidence adversarial samples. In this study, we propose Graph-Guided Testing (GGT) for adversarial sample detection to overcome these aforementioned challenges. GGT generates pruned models with the guide of graph characteristics, each of them has only about 5% parameters of the mutated model in MMT, and graph guided models have higher diversity. The initial experiments on CIFAR10 validate that GGT performs much better than MMT with respect to both effectiveness and efficiency.
SOINN-Based Abnormal Trajectory Detection for Efficient Video Condensation

With the evolution of video surveillance systems, the requirement of video storage grows rapidly; in addition, safe guards and forensic officers spend a great deal of time observing surveillance videos to find abnormal events.As most of the scene in the surveillance video are redundant and contains no information needs attention, we propose a video condensation method to summarize the abnormal events in the video by rearranging the moving trajectory and sort them by the degree of anomaly.Our goal is to improve the condensation rate to reduce more storage size, and increase the accuracy in abnormal detection.As the trajectory feature is the key to both goals, in this paper, a new method for feature extraction of moving object trajectory is proposed, and we use the SOINN (Self-Organizing Incremental Neural Network) method to accomplish a high accuracy abnormal detection.In the results, our method is able to shirk the video size to 10% storage size of the original video, and achieves 95% accuracy of abnormal event detection, which shows our method is useful and applicable to the surveillance industry.
Enhanced Temporal Graph Embedding for Identifying Fraudulent Transactions on Transaction Networks

Electronic payments have penetrated into every aspect of our lives, and the transaction records of users form transaction networks. Fraudulent transactions in the transaction network bring huge losses to the society, and it is important to identify fraudulent transactions among them. Most existing methods focus on anomaly detection of static graphs, ignoring the information of temporal changes of graphs and transaction payment scenarios. In this paper, we propose a missing prediction dynamic graph anomaly transaction detection model on transaction networks. It consists of three parts: (1) feature extraction layer, in which single evaluation features and common evaluation features are extracted from the ego-net of nodes, and labels of edges are directly used for embedding vector generation. (2) missing information prediction layer, which uses domain models to predict missing information in the temporal graph and complements node features. (3) network evolution layer, which uses GAT in GRU to aggregate graph structure information and to evolve missing node information from both forward and reverse directions. Anomalous edge classification experiments were conducted on three real datasets, and the proposed method outperformed the compared baseline methods.
Bayesian hierarchical finite mixture of regression for histopathological imaging‐based cancer data analysis

Cancer is heterogeneous, and for seemingly similar cancer patients, the associations between an outcome/phenotype and covariates can be different. To describe such differences, finite mixture of regression (FMR) and other modeling techniques have been developed. "Classic" FMR analysis has usually been based on clinical, demographic, and molecular variables. More recently, histopathological imaging data-which is a byproduct of biopsy and enjoys broader data availability and higher cost-effectiveness-has been increasingly used in cancer modeling, although it is noted that its application to cancer FMR analysis still remains limited. In this article, we further advance cancer FMR analysis based on histopathological imaging data. Significantly advancing from the existing analyses under heterogeneity and homogeneity, our goal is to simultaneously use two types of histopathological imaging features, which are extracted based on domain-specific biomedical knowledge and using automated signal processing software, respectively. A significant modeling/methodological advancement is that, to reflect the "increased resolution" of the second type of imaging features over the first type, we impose a hierarchy in the mixture structures. An effective and flexible Bayesian approach is proposed. Simulation shows its competitiveness over several alternatives. The TCGA lung cancer data is analyzed, and interesting heterogeneous structures different from using the alternatives are found. Overall, this study provides a new venue for FMR analysis for cancer and other complex diseases.