Reviewerly/exHarmony huggingface dataset:
L-Snet: From Region Localization To Scale Invariant Medical Image Segmentation

Coarse-to-fine models and cascade segmentation architectures are widely adopted to solve the problem of large scale variations in medical image segmentation. However, those methods have two primary limitations: the first-stage segmentation becomes a performance bottleneck; the lack of overall differentiability makes the training process of two stages asynchronous and inconsistent. In this paper, we propose a differentiable two-stage network architecture to tackle these problems. In the first stage, a localization network (L-Net) locates Regions of Interest (RoIs) in a detection fashion; in the second stage, a segmentation network (S-Net) performs fine segmentation on the recalibrated RoIs; an RoI recalibration module between L-Net and S-Net eliminating the inconsistencies. Experimental results on the public dataset show that our method outperforms state-of-the-art coarse-to-fine models with comparable computation.
The Challenge of Oral English Education under the Influence of the Belt and Road

The traditional English teaching model based on the theory of learning motivation cannot solve the problem of slow improvement of spoken English and shortage of professional talents under the influence of the Belt and Road. Therefore, the semantic information model of oral English teaching is constructed. Based on the analysis of the needs and challenges of English higher education under the influence of the Belt and Road, the semantic information model of EML is used to abstract the learning process of spoken English into multiple roles and to realize the reform of oral English teaching. Through the separation of the design and technology of the learning unit in UML-based collaborative educational resource development, the cooperation between English oral teaching and technical personnel is guaranteed; the required resources are retrieved through Top-k retrieval based on semantic information. Finally, the objectives, contents, methods, models, and systems of personnel training have been comprehensively reformed to achieve the cultivation and level improvement of spoken English talents. The experimental results show that the constructed model can improve the enthusiasm of oral English learning in Chinese universities under the influence of the Belt and Road and promote the rapid improvement of oral English performance.
The activity of Irkutsk branch of Krasnoyarsk Specialized School of Militia of the Ministry of Internal Affairs of the Soviet Union: historical-pedagogical aspect

The subject of research of this research is the process of establishment and development of Irkutsk branch of the Krasnoyarsk Specialized School of Militia of the Ministry of Internal Affairs of the Soviet Union during the 1989&amp;ndash;1991, and its subsequent professional-pedagogical transformation. The author dwells on the question of historical-pedagogical peculiarities of the initial stage of recruiting the academic staff of the branch by the scientific personnel. The author explores the professional peculiarities in carrying out educational, service and combat activity of the permanent and nonpermanent staff of the specialized school. In the course of scientific research, the author reveals and analyzes the factors that affected the establishment and development of Irkutsk branch of Krasnoyarsk Specialized School of Militia of the Ministry of Internal Affairs of the Soviet Union during the transformation of the political system in Russia; as well as explores the peculiarities of implementing &amp;nbsp;pedagogical activity at the initial stage of the educational institution. The examples of interaction of the cadets and employees of the branch with the divisions of practical authorities. The conducted research is valuable for wide audience, as the establishment and development of departmental education of the system of the Ministry of Internal Affairs of the Soviet Union in Baikal region has become a significant event for the educational system of the entire Siberian region.
An automated machine learning tool for breast cancer diagnosis for healthcare professionals

The paper proposes a hybrid metaheuristic algorithm known as harmony search and simulated annealing (HS-SA) for accurate and precise breast malignancy disclosure by integrating harmony search (HS) and simulated annealing (SA) optimisation methods. An enhanced wavelet-based contourlet transform (WBCT) procedure for mining the highlights of the region of interest (ROI) is explored, that allows execution upgradation over other standard procedures. The anticipated HS-SA algorithm aims to reduce the feature dimensions and assemble at the unparalleled optimal feature subset. The SVM classifier fed with the picke.d feature subsets and assisted by varied kernel functions upheld its classification capacities in contrast with the conformist machine learning classification and optimisation methods. The portrayed computer-aided diagnosis (CAD) model is confronted by evaluating its learning capability on two different breast mammographic datasets i) benchmark BCDR-F03 dataset and ii) local mammographic dataset. Preliminary propagations, experimental outcomes, and quantifiable assessments likewise demonstrate that the proposed model is pragmatic and favourable for the automated breast malignancy findings with optimal performance and fewer overheads. The discoveries show that the proposed CAD system (HS-SA+Kernel SVM) is superior to various characterisation accuracy techniques with an accuracy of 99.89% for the local mammographic dataset and 99.76% for benchmark BCDR-F03 dataset, AUC of 99.41% for the local mammographic dataset and 99.21% for reference BCDR-F03 dataset while keeping the element space restricted to only seven feature subsets and computational prerequisites as low as is judicious.
Construction and Implementation of "1331" Classroom Teaching Mode

Since 2017, Chengdu Neusoft Institute Of Information has begun to implement the "reform of the 1331 classroom teaching model", firmly grasp the "45-minute classroom" main battleground, implement the "1331" classroom reform model, adhere to the student-centered, promote the integration of three lessons, three books, and build a multi-assessment evaluation system to explore the direction of undergraduate classroom education reform. This teaching reform has fully mobilized the enthusiasm of students, increased their participation and creativity, and realized the transformation of traditional classrooms into efficient schools.
Sketch Recognition for Interactive Game Experiences Using Neural Networks

Human freehand sketches can provide various scenarios to the interfaces with their intuitive, illustrative, and abstract nature. Although freehand sketches have been powerful tools for communication and have been studied in different contexts, their capacity to create compelling interactions in games is still under-explored. In this study, we present a new game based on sketch recognition. Specifically, we train various neural networks (Recurrent Neural Networks and Convolutional Neural Networks) and use different classification algorithms (Support Vector Machines and k-Nearest Neighbors) on sketches to create an interactive game interface where the player can contribute to the game by drawing. To measure usability, technology acceptance, immersion, and playfulness aspects, 18 participants played the game and answered the questionnaires composed of four different scales. Technical results and user tests demonstrate the capability and potential of sketch integration as a communication tool to construct an effective and responsive visual medium for novel interactive game experiences.
Learning to Trade from Zero-Knowledge Using Particle Swarm Optimization

Competitive co-evolutionary particle swarm optimization (CEPSO) algorithms have been developed to train neural networks (NNs) to predict trend reversals. These approaches considered the optimization problem, i.e. training of the NNs to maximize net profit and to minimize risk, as a static optimization problem. Based on the dynamic nature of the financial stock market, this paper proposes that the training should rather be treated as a dynamic optimization problem. A new dynamic CEPSO is proposed and used to train a NN on technical market indicators to predict trade actions. In addition, this paper incorporates approaches to combat saturation of the activation functions – an aspect neglected in previous research. The dynamic CEPSO is evaluated and compared with the static CEPSO approach, a buy-and-hold strategy, and a rule-based strategy. Results show that the new CEPSO performs significantly better on a selection of South African stocks.
Security Assessment of Selectively Encrypted Visual Data: Iris Recognition on Protected Samples

Security assessment of partially encrypted visual data is known to be difficult. We show that iris recognition performance on encrypted sample data turns out to be a good predictor for protection strength of encryption schemes for visual data, in particular in settings where image structure and intelligibility is preserved to some extent. This is of particular interest, as common image quality metrics used for this task exhibit specifically poor prediction quality in this setting.
Merkle Based Cryptographic Tree for Preventing Hash Collisions in Data Deduplication Mechanism on Servers

Due to the explosive rise in Digital Data content, the rise in the demand of new storage and network equipment is getting common with an increasing need for more cost-effective use of storage and network bandwidth for data transfer. Cloud services are also growing at an explosive rate as it provides cost efficient architectures. Such cloud architectures provide transmission and storage in multi user environment and works usually on pay per use model we only have a need to pay for what we use. For saving resources on cloud environment many cloud service provider use Data Duplication eradication mechanism. Here, Our approach is focusing on the Merkle Tree approach for eradication of Data Duplication.
RETRACTED CHAPTER: Using Bidirectional LSTMs with Attention for Categorization of Toxic Comments

The online atmosphere is conducive for building connections with people all around the world, surpassing geographical boundaries. However, accepting participation from everyone is at the cost of compromising with abusive language or toxic comments. Limiting people from taking part in the discussions is not a viable option just because of misbehaving users. The proposed framework in this research harnesses the power of deep learning to enable toxic online comment recognition. These comments are further categorized using natural language processing tools.
UGTO: Uncommon Words and Proper Nouns

The conventional position-based tagging schemes that previous research used to model named entities suffer from the problem of inconsistent tag assignment. To overcome the problem of inconsistent tag assignment, we define a constituent-based tagging scheme termed UGTO scheme to model named entities under conditional random fields with uncommon words and proper nouns as features. In addition, many researchers jointly model multiple linguistic tasks with an implicit assumption that these individual tasks can enhance each other via the joint modeling. Before conducting research on jointly modeling multiple tasks, however, such researchers hardly examine whether such assumption is true or not. In this chapter, we empirically examine whether named entity classification improves the performance of named entity recognition as an empirical case of examining whether semantics improves the performance of a syntactic task. Evaluation shows that named entity recognition does not lie at the semantic level and is not a semantic task, instead, it is a syntactic task, and that the joint modeling of named entity recognition and classification does not improve the performance of named entity recognition. Evaluation also demonstrate that traditional hand-crafted-feature models can achieve state-of-the-art performance in comparison with the auto-learned-feature model on named entity recognition.
Approaches used to Verify and Validate a Software Testing Ontology as an Artifact

Verification and Validation (V&V) are undoubtedly two key aspects of the artifact construction process. V&V entails activities, methods, tools, and ultimately strategies for ensuring quality when developing artifacts. Software engineering production lines, stakeholders and researchers produce diverse artifacts. For example, researchers may build ontologies, which can be both conceptualized and implemented artifacts. Ontologies are playing a central role in many application domains in particular, as well as in software and knowledge engineering disciplines in general. To show the applicability of V&V approaches, this work considers a software testing ontology as an artifact. This was developed following the Design Science Research methodology in the context of a layered ontological architecture. This paper illustrates the V&V approaches used, involving activities and methods for static and dynamic testing, evaluation and assessment. It addresses V&V approaches for the ontology conceptualization, as well as a functional dynamic testing method for its implementation.
Weakly Supervised Domain Adaptation using Super-pixel labeling for Semantic Segmentation

Deep learning for semantic segmentation requires a large amount of labeled data, but manually annotating images are very expensive and time consuming. To overcome the limitation, unsupervised domain adaptation methods adapt a segmentation model trained on a labeled source domain (synthetic data) to an unlabeled target domain (real-world scenes). However, the unsupervised methods have a poor performance than the supervised methods with target domain labels. In this paper, we propose a novel weakly supervised domain adaptation using super-pixel labeling for semantic segmentation. The proposed method reduces annotation cost by estimating a suitable labeling area calculated from the Entropy-based cost of a previously learned segmentation model. In addition, we generate the new pseudo-labels by applying fully connected Conditional Random Field model over the pseudo-labels obtained using an unsupervised domain adaptation. We show that our proposed method is a powerful approach for reducing annotation cost.
A mean preset Retinex algorithm for color image enhancement

Abstract: Image mean value is the most commonly used and most important indicator of image quality evaluation in image enhancement. Generally, the mean value of enhanced image is uncertain. In this paper, a Mean-Preset Retinex(MPR) algorithm is proposed. That is to say, here, image mean value is not only used as an evaluating indicator, but also as an image enhancement parameter to design the improved algorithm. At firstly, we preset the mean value of enhanced image. Secondly, according to the preset mean value, we calculate the pixel value which the median value of original image is pre-stretched. Thirdly, we use piece-wise linear stretch method to stretch and enhance the original image. In the experiment, we compare the performance and effect of enhanced image with MPR, MSR, MSR-CR, etc. Experimental results show that MPR has better enhancement effect and performance than other methods, and has good robustness and applicability.
Quantum Tomography of Entangled Spin-Multiphoton States

We present a method for quantum tomography of multiqubit states. We apply the method to spin-multiphoton states, which we generate using a quantum dot based device. Periodic excitation of a quantum dot confined spin deterministically generates strings of entangled photons in a cluster state, threefold faster than previously demonstrated. Our tomography method uses time-resolved polarization-sensitive multiphoton correlation measurements to measure both the polarization of the confined spin and that of the emitted photons. We develop an edge-sensitive gradient-descent algorithm and apply it to the acquired data to map the periodic physical process that generates the cluster state. We utilize our tomographic method to optimize our quantum dot based device and show that the enhanced generation rate increases the entanglement robustness of the generated multiqubit state.
ANALYSIS OF THE WEB APPLICATION ON BAYES’ THEOREM CONSIDERING DATA SCIENCE AND TECHNOLOGICAL ACCEPTANCE MODEL

This mixed research aims to design and implement the Web Application on Bayes' Theorem (WABT) in the Statistical Instrumentation for Business subject.WABT presents the procedure to calculate the probability of Bayes' Theorem through the simulation of data about the supply of products.Technology Acceptance Model (TAM), machine learning and data science are used to analyze the impact of WABT on the educational process.The results of machine learning (60%, 70% and 80% of training) indicate that WABT positively influences the Motivation, Autonomy, Personalized learning and Active role.Data science identifies predictive models of the impact of WABT on the teaching and learning process through the decision tree technique.In addition, WABT is a pleasant, simple, useful and innovative web tool for the educational field.Finally, teachers can use TAM model, data science and machine learning in order to identify the impact of digital tools on the educational process.
A Gestão da Informação como facilitadora do empoderamento psicológico e da melhoria do desempenho profissional

Information is essential to the organizations. They structure and classify it based on their organization, functions and activities, that is, based on the document production, the information flows between departments, the safeguarding and proof that this information constitutes. At Goweb Agency, Lda., most documents existed only for quality certification purposes. In order to improve information management in the organization, all documents were organized in a valid, intuitive way, based on the employees' information needs. By incorporating them in the routine tasks of daily life and encouraging their use, creating and adapting the sharing and management of information in the company's activities, there was a decrease in internal tension between employees, an increase in their level of satisfaction, their efficiency and effectiveness and a growing satisfaction of the company's customers, due to the performance and quality of the delivered work.
Expert System To Identify Insomnia Problems In Adults Using Forward Chaining Method

Insomnia is a symptom that is often experienced by adults of productive age, such as having difficulty starting to sleep, often waking up at night. With the development of technology, most of the jobs have involved technology. Expert systems are artificial intelligence that is growing rapidly in the world of technology. With an expert system, a person can be helped according to their needs when it is difficult to meet directly with the expert. The purpose of this study was to make it easier for researchers to identify insomnia problems based on the symptoms experienced by the adults around them. The research method used is the forward chaining method. while the data collection technique is quantitative data technique. The desired result of this study is the reduction of a person affected by insomnia problems and the Expert System can provide solutions to reduce Insomnia Problems such as relaxation music before consulting a doctor.
Selecting an Iconic Pose From an Action Video

This paper presents a method for selecting an iconic pose frame from an action video. An iconic pose frame is a frame showing a representative pose, distinct from other actions. We first extract a diverse set of keyframes from the video using unsupervised video summarization. A classification loss ensures that the selected frames retain high action classification accuracy. To find iconic poses, we introduce two loss terms, an Extreme Pose Loss, encouraging selecting poses far from the mean pose, and a Frame Contrastive Loss, which encourages poses from the same action to be similar. In a user preference study on UCF-101 videos we show that the automatically selected iconic pose keyframes are preferred to manually selected ones in 48% of cases.
Cost-Based Application Placement Optimization for Integrated Energy System using Docker

In the integrated energy system (IES), distributed energy, flexible load and electric vehicle are widely involved in the energy production, consumption and other aspects of the power system, which makes it an important issue to optimize the deployment of energy management application. Therefore, this paper proposes cost-based application placement method using computing resource containers based on Docker technology, which can effectively reduce the deployment cost of application placement in IES. We first characterize the task components and their connections in energy management application. According to the architecture of physical machine using Docker, we construct the operation model including computation model and communication model Besides, to evaluate the application placement schemes, we propose a cost-based evaluation metrics considering the cost caused by task execution and data transmission. Simulation verifies the rationality and effectiveness of our proposed method.
Multi-pathway feature integration network for salient object detection

Saliency detection is a computer vision task that has been studied for a long time. Although the recent methods continuously improve detection accuracy, most of them do not consider the interactions of different feature layers sufficiently, and deep- and shallow-level features are not fused effectively. To combine the advantages of multi-layer features for detection, we propose a novel multi-pathway feature integration network (MFINet) by extracting and fusing information from multiple feature layers. Different from the top-down and bottom-up fusion styles, our proposed feature fusion is divided into two stages and three pathways are finally formed. All three pathways integrate multiple original backbone feature layers, where they integrate features in three different orders. The diversity of feature fusion compensates for the limitations of the top-down and bottom-up fusion approaches, effectively preventing the loss of useful information in the transmission process. In addition, we propose a cross-feature hierarchical fusion module. This module combines the saliency cues in two feature layers adaptively and reduces the representation of redundant information. Therefore, multiple such modules are embedded in the MFINet to enable the network to further learn salient object information accurately. To strengthen the response of boundary information in the salient object information, we also propose a boundary information enhancement strategy. Unlike existing methods that use an independent branch to extract boundary features, we directly capture boundary features from a boundary map. Experimental results on 5 benchmark datasets show that our method achieves better performance in different scenarios, consistently outperforming 12 state-of-the-art methods.
Rigidity, Graphs and Hausdorff Dimension

A set of $$k+1$$ points in Euclidean space is called a $$(k+1)$$ -point configuration. Two configurations are congruent if they are equal up to an affine isometry. Given a compact subset E of $$\mathbb R^d$$ , $$d\ge 2$$ of Hausdorff dimension greater than $$d-\frac{1}{k+1}$$ we prove that the Lebesgue measure of noncongruent $$(k+1)$$ -point configurations in E is positive, for $$k>d$$ , complementing the results of [11] for $$k\le d$$ .