Reviewerly/exHarmony huggingface dataset:
Computerized classification method for histological classification of masses on breast ultrasonographic images using convolutional neural networks with ROI pooling

Abstract It can be difficult for clinicians to correctly determine histological classifications of masses on breast ultrasonographic images. The purpose of this study was to develop a computerized classification method for histological classification of masses on breast ultrasonographic images using convolutional neural networks (CNN) with a ROI pooling that analyzes feature maps focusing on the mass region. Our dataset consisted of 585 breast ultrasonographic images obtained from 585 patients. It included 288 malignant masses (218 invasive and 70 noninvasive carcinomas) and 297 benign masses (115 cysts and 182 fibroadenomas). In this study, we developed a modified CNN model based on ResNet‐18, in which the ROI pooling and two fully connected layers with a softmax function were introduced after the second and fourth residual block on ResNet‐18, respectively. The proposed CNN model was employed to distinguish among four different types of histological classifications for masses. A three‐fold cross validation method was used for training and testing the proposed CNN model. The average accuracy, sensitivity, specificity, positive predictive value and negative predictive value for the proposed CNN model were 81.7%, 91.0%, 91.2%, 91.0%, and 91.2%, respectively. Those results were substantially greater than those with ResNet‐18 (70.3%, 83.0%, 87.2%, 86.3%, and 84.1%).
DOT-VAE: Disentangling One Factor at a Time

As we enter the era of machine learning characterized by an overabundance of data, discovery, organization, and interpretation of the data in an unsupervised manner becomes a critical need. One promising approach to this endeavour is the problem of Disentanglement, which aims at learning the underlying generative latent factors, called the factors of variation, of the data and encoding them in disjoint latent representations. Recent advances have made efforts to solve this problem for synthetic datasets generated by a fixed set of independent factors of variation. Here, we propose to extend this to real-world datasets with a countable number of factors of variations. We propose a novel framework which augments the latent space of a Variational Autoencoders with a disentangled space and is trained using a Wake-Sleep-inspired two-step algorithm for unsupervised disentanglement. Our network learns to disentangle interpretable, independent factors from the data “one at a time”, and encode it in different dimensions of the disentangled latent space, while making no prior assumptions about the number of factors or their joint distribution. We demonstrate its quantitative and qualitative effectiveness by evaluating the latent representations learned on two synthetic benchmark datasets; DSprites and 3DShapes and on a real datasets CelebA.
Collaborative Multiple-Student Single-Teacher for Online Learning

Knowledge distillation is a popular method where a large trained network (teacher) is implemented to train a smaller network (student). To decrease the need for training a much larger network (teacher) for real time application, one student self-knowledge distillation was introduced as a solid technique for compressing neural networks specially for real time applications. However, most of the existing methods consider only one type of knowledge and apply one-student one-teacher learning strategy. This paper presents a collaborative multiple-student single-teacher system (CMSST). The proposed approach is based on real time applications that contain temporal information, which play an important role in understanding. We designed a backbone old student network with target complexity for deployment, during training, once the old student provides high-quality soft labels to guide the hierarchical new student, it also offers the opportunity for the new student to make meaningful improvements based on the students’ revised feedback via the shared intermediate representations. Moreover, we introduced soft target label smoothing technique to the CMSST. Experimental results showed that the accuracy can be improved on newly developed teacher knowledge distillation by 1.5% on the UCF-101. Also the accuracy was improved by 1.15% compared to normal huge teacher knowledge distillation on CIFAR100 dataset.
To Tree or Not to Tree? Assessing the Impact of Smoothing the Decision Boundaries

When analyzing a dataset, it can be useful to assess how smooth the decision boundaries need to be for a model to better fit the data. This paper addresses this question by proposing the quantification of how much should the 'rigid' decision boundaries, produced by an algorithm that naturally finds such solutions, be relaxed to obtain a performance improvement. The approach we propose starts with the rigid decision boundaries of a seed Decision Tree (seed DT), which is used to initialize a Neural DT (NDT). The initial boundaries are challenged by relaxing them progressively through training the NDT. During this process, we measure the NDT's performance and decision agreement to its seed DT. We show how these two measures can help the user in figuring out how expressive his model should be, before exploring it further via model selection. The validity of our approach is demonstrated with experiments on simulated and benchmark datasets.
Quantum Theory of Fuzzy Events—Reformation and Redevelopment of Quantum Theory

The goal of this study is to extend quantum theory to the field of fuzzy phenomena. Using the idea and method of fuzzy mathematics, the quantum probability theory of fuzzy event is proposed, with the form of fuzzy event route integral, membership degree amplitude, fuzzy field function, Green function, physical quantity and fuzzy diagram. This theory changes quantum mechanics, making it a special instance of the former. Under some conditions, this theory breaches unitarity, gauge invariance, probability conservation, and information conservation, rendering these concepts approximate. This new theory, which does not require renormalization and can naturally produce meaningful results that are consistent with the experiments, is the proper theory for describing microscopic high-speed phenomena, whereas quantum mechanics is only a proper theory for describing microscopic low-speed phenomena. Because this theory is not divergent under the conditions that there is no renormalization and an infinite number of offsetting terms, it can constitute the theoretical framework required for gravity quantization.
A Study on the Efficiency of Private School Teachers' Remedies for Rights: Focus on the Teachers' Appeal Review System

Education is the key responsibility to carry out the state because the achievement of education is the most important factor in determining the future of the country. Therefore, the status of teachers in charge of education should be guaranteed, and the educational activities of teachers should be protected and respected. And if the status and authority of the teacher are violated illegally, the authority must be protected and respected promptly and accurately. Under the current law, if a request for a petition is filed by the Teachers' Appeals Review Committee or if it is dissatisfied with the decision of the petition, the administrative litigation may be filed and remedied by the defendant, and the legal relationship of the private school teachers may be remedied through civil litigation. However, there are cases where the right to dispose of is not able to promptly and accurately remedy the right by neglecting the decision of appeal or filing an administrative lawsuit against the decision. Therefore, in order to secure the effectiveness of the decision of the appeal examination and to expand the right remedy, the objectivity of the appeal examination request should be expanded, the legitimacy of the disposition should be secured, the right of disposition should be prevented from abusing or being neglected, the implementation of the appeal examination decision should be enforced, and further punishment should be applied effectively. In addition, if the compensation through civil litigation becomes an issue, it will be possible to introduce a mediation system to promote rapid and economic rights relief. These teachers' rights relief is not only effective because the law and system are revised and newly introduced. Based on the importance of education, the right to dispose of the disposition should secure procedural and content legality, and in the teacher appeal examination system or litigation, it is necessary to strictly examine the disciplinary disposition or unfavorable disposition to improve the effect of legislation and system.
Deploying wearable sensors for pandemic mitigation: A counterfactual modelling study of Canada’s second COVID-19 wave

Wearable sensors can continuously and passively detect potential respiratory infections before or absent symptoms. However, the population-level impact of deploying these devices during pandemics is unclear. We built a compartmental model of Canada’s second COVID-19 wave and simulated wearable sensor deployment scenarios, systematically varying detection algorithm accuracy, uptake, and adherence. With current detection algorithms and 4% uptake, we observed a 16% reduction in the second wave burden of infection; however, 22% of this reduction was attributed to incorrectly quarantining uninfected device users. Improving detection specificity and offering confirmatory rapid tests each minimized unnecessary quarantines and lab-based tests. With a sufficiently low false positive rate, increasing uptake and adherence became effective strategies for scaling averted infections. We concluded that wearable sensors capable of detecting presymptomatic or asymptomatic infections have potential to help reduce the burden of infection during a pandemic; in the case of COVID-19, technology improvements or supporting measures are required to keep social and resource costs sustainable.
Stable optimal self-tuning interval type-2 fuzzy controller for servo position control system

An optimal self-tuning interval type-2 fuzzy controller for servo position control systems is reported here. To achieve precise positioning of the actuator, input scaling factors of an interval type-2 fuzzy proportional-integral controller are updated online depending on the latest operating conditions in terms of closed loop tracking error and change of error. To ensure desired performance, input scaling factors are obtained by adaptive cuckoo search-based optimisation algorithm. Efficacy of the proposed scheme is substantiated through performance comparison with recently reported peak observer based and online self-tuning based interval type-2 fuzzy PID, interval type-2 fuzzy PI, and also type-1 fuzzy PI controllers through simulation study along with real-time validation on a DC servo position control system. Lyapunov function-based stability analysis for the proposed controller is also provided.
Research on privacy fraud detection of Logistic regression based on homomorphic encryption

Fraud detection is an integral part of financial security monitoring tool; however, the traditional fraud detection method cannot detect the existing malicious fraud, and the clouds will produce data revealing that the risk of fraud detection system can not protect the privacy of detected object, so the fraud detection data privacy security becomes a significant problem,Homomorphic encryption as a demonstrable cryptography cloud privacy computing outsourcing scheme can ensure that cloud computing can perform ciphertext polynomial calculation under the dense state data without direct contact with the accurate data of users, so as to ensure data privacy security. Aiming at the data privacy security problems in the process of fraud detection, this paper combined homomorphic encryption and Logistic regression fraud detection technology to study the Logistic regression fraud detection algorithm under homomorphic ciphertext and constructed a cloud privacy fraud detection method based on customer service and cloud computing services. CKKS encryption scheme is used to encrypt the fraud data set and realize the Logistic regression fraud detection algorithm under ciphertext. The experiment proves that the difference between the fraud detection accuracy on ciphertext and plaintext is less than 3%. Under the condition of ensuring the privacy of sensitive data to be detected, the effect of the fraud detection model is not affected.
Concepts and Mechanisms of Negotiation Derived from Experiences in the Electronic Milieu

In this paper, we address the relevance of virtual worlds for negotiation using the example of Second Life and AltspaceVR; we take into account mindset issues and an avatar’s influence on this process. The concept of negotiation is related here to the concept of a networked society to describe actions undertaken between two or more individuals, groups, and/or organizations. The network is a milieu for negotiating with oneself and with others. Negotiating in a networked space can be an opportunity for self-exploration, subversion, and compensation for the limitations of physical reality, and it also involves background problems and the displacement of power. The term “negotiation” is used in online communities to describe interactions between people who are not physically present but interact with each other through some technical devices, such as the telephone or the Internet. In the waves of technological development, how people organize their lives and behave is a question of convenience. Virtual worlds, in which the human is extended by an avatar (and the avatar by the electronic space), are now being transformed into worlds where the human is just the avatar. The avatar is in the same space the human is in, i.e., in the physical world. This transformation (in fact, a paradigm shift) involves changing the habits of users, who are now adopting new habits in the form of negotiating the physical world with the values and habits of the virtual world.
Improving Students’ Performance in Oral Reading Through Precision Teaching Approach

This study investigated the effectiveness of Precision Teaching in enhancing the oral reading performance of students at a public high school in Pasig City. Ten students participated in the study. They were the students whose identified reading level was frustration. The Phil-IRI Manual 2018 served as the assessment tool. Pretests and posttests were all taken from the Phil-IRI. In addition, this study employed a mixed-method design to glean the data from the participants. The intervention was provided to the students for four weeks, with two hours allotted per session. For the quantitative component, paired sample t-test and mean were utilized. Thematic analysis was used in analyzing the interview data. Results of the study revealed that there is a significant difference between the pretest and posttest scores of students in oral reading and comprehension. On the other hand, there was no significant difference in the reading speed of students based on their pretest and posttest scores. Based on the thematic analysis of the semi-structured interviews, two major themes were divulged: the learning that took place and the challenges the students encountered during the entire intervention program. The results of this study should be seen in light of the limitations such as the number of participants and the length of the provided intervention program. Keywords: Precision Teaching; oral reading performance; reading speed; reading comprehension
Learning Ontology Classes from Text by Clustering Lexical Substitutes Derived from Language Models1

Many tools for knowledge management and the Semantic Web presuppose the existence of an arrangement of instances into classes, i. e. an ontology. Creating such an ontology, however, is a labor-intensive task. We present an unsupervised method to learn an ontology from text. We rely on pre-trained language models to generate lexical substitutes of given entities and then use matrix factorization to induce new classes and their entities. Our method differs from previous approaches in that (1) it captures the polysemy of entities; (2) it produces interpretable labels of the induced classes; (3) it does not require any particular structure of the text; (4) no re-training is required. We evaluate our method on German and English WikiNER corpora and demonstrate the improvements over state of the art approaches.
Density Maximizers of Layered Permutations

A permutation is layered if it contains neither $231$ nor $312$ as a pattern.It is known that, if $\sigma$ is a layered permutation, then the density of $\sigma$ in a permutation of order $n$ is maximized by a layered permutation. Albert, Atkinson, Handley, Holton and Stromquist [Electron. J. Combin. 9 (2002), #R5] claimed that the density of a layered permutation with layers of sizes $(a,1,b)$ where $a,b\geq2$ is asymptotically maximized by layered permutations with a bounded number of layers, and conjectured that the same holds if a layered permutation has no consecutive layers of size one and its first and last layers are of size at least two.&#x0D; We show that, if $\sigma$ is a layered permutation whose first layer is sufficiently large and second layer is of size one, then the number of layers tends to infinity in every sequence of layered permutations asymptotically maximizing the density of $\sigma$. This disproves the conjecture and the claim of Albert et al. We complement this result by giving sufficient conditions on a layered permutation to have asymptotic or exact maximizers with a bounded number of layers.
Collaborative environment in inclusive education and professional competencies

The article is devoted to the issue of organizing a collaborative educational environment in the context of inclusive education at a University. The analysis of the study of theoretical sources and practical experience allows us to conclude that there is insufficient training of teachers in the field of inclusive education, the formation of professional competencies. The authors give an essential characteristic of the concept of "collaborative environment", which is an innovative approach in the preparation of future bachelors of education on the basis of collaborative interdepartmental interaction. Collaboration means the joint solution of the task of training personnel on the basis of pedagogical interaction. The priority aspects of creating collaborative education include the use of the possibilities of combining individually-oriented teaching and communicational and educational technologies presented in various forms. Collaboration is also considered as a specific form of interaction and cooperation in the educational process of teachers and students on the example of teaching students with special educational needs according to the educational program "Musical Education".
Innovation in women-led firms: an empirical analysis

The availability of resources is paramount for innovation in women-led firms. We define a women-led firm where a chief executive officer (CEO) is a woman. We examine how nature – a pervasive and arbitrary type of institutional environment as well as access to resources is associated with different propensity to innovate between women and men-led firms. Using micro-level cross-country data of 12,412 firms and a reduced sample of 5,052 firms during 2008–2015 and across 75 economies, we find that the differences in risk perception between women and men-led firms ae not associated with innovation propensity. Instead, the availability of resources proxied by a country’s fiscal freedom and availability of internal resources lead to an increase in innovation in women-led firms. The relationship is non-linear and increases with the availability of finance. This work has important policy implications for the role of institutions to spur innovation in women-led firms.
More Diverse Training, Better Compositionality! Evidence from Multimodal Language Learning

Artificial neural networks still fall short of human-level generalization and require a very large number of training examples to succeed. Model architectures that further improve generalization capabilities are therefore still an open research question. We created a multimodal dataset from simulation for measuring the compositional generalization of neural networks in multimodal language learning. The dataset consists of sequences showing a robot arm interacting with objects on a table in a simple 3D environment, with the goal of describing the interaction. Compositional object features, multiple actions, and distracting objects pose challenges to the model. We show that an LSTM-encoder-decoder architecture jointly trained together with a vision-encoder surpasses previous performance and handles multiple visible objects. Visualization of important input dimensions shows that a model that is trained with multiple objects, but not a model trained on just one object, has learnt to ignore irrelevant objects. Furthermore we show that additional modalities in the input improve the overall performance. We conclude that the underlying training data has a significant influence on the model’s capability to generalize compositionally.
Design of a Virtual Cocaine Consumption Scenario for Craving Study

By providing any wanted situation without the risks associated to it, Virtual Reality (VR) bears a strong potential for Cue Exposure Therapy (CET) related to psychoactive substance use disorders (SUD), as it allows both exposing participants to a great variety of controlled stimuli and making them involved through a strong sense of presence. However, while Virtual Reality Exposure Therapy (VRET) has already been used successfully in the treatment of several disorders (e.g. tobacco dependence, agoraphobia, social phobias, post-traumatic stress disorder, obsessive-compulsive disorder), its effectiveness still needs to be demonstrated for the treatment of cocaine-related SUD. Most notably, its ability to induce craving must be evaluated. In this paper, we describe the design and development of a virtual environment made for the treatment of substance use disorder with cocaine users through CET. This environment is also presented in a video (https://vimeo.com/568320310).
Stroke Extraction Algorithm of Clerical Script in Han Dynasty Based on Contour: Take “Stele of Cao Quan” as an Example

Under natural conditions, the inscription will be damaged by corrosion and oxidation, which is very unfavorable for the inheritance of this cultural heritage. Therefore, how to protect and study ancient calligraphy inscriptions by modern scientific and technological methods is a scientific and technological problem before us. In this paper, taking “Stele of Cao Quan” as an example, a stroke extraction algorithm of clerical script in Han Dynasty based on contour is proposed. Starting from the Chinese character skeleton, the basic skeleton is extracted by parallel thinning algorithm, and the redundant points in the skeleton are eliminated by matching with the construction template. In addition, the contour tracking thinning algorithm is analyzed. Aiming at the problems that the starting point is difficult to determine and the tracking process is complicated, the method of determining the tracking starting point is given, and some problems existing in the tracking process are solved. The experimental results show that the accuracy of regular script, running script, and clerical script is 88.35%, 86.71%, and 84.09%, respectively. Experiments show that the contour-based clean script stroke extraction algorithm can correctly extract clean script strokes.