Reviewerly/exHarmony huggingface dataset:
Closed sequential pattern mining for sitemap generation

Abstract A sitemap represents an explicit specification of the design concept and knowledge organization of a website and is therefore considered as the website’s basic ontology. It not only presents the main usage flows for users, but also hierarchically organizes concepts of the website. Typically, sitemaps are defined by webmasters in the very early stages of the website design. However, during their life websites significantly change their structure, their content and their possible navigation paths. Even if this is not the case, webmasters can fail to either define sitemaps that reflect the actual website content or, vice versa, to define the actual organization of pages and links which do not reflect the intended organization of the content coded in the sitemaps. In this paper we propose an approach which automatically generates sitemaps. Contrary to other approaches proposed in the literature, which mainly generate sitemaps from the textual content of the pages, in this work sitemaps are generated by analyzing the Web graph of a website. This allows us to: i) automatically generate a sitemap on the basis of possible navigation paths, ii) compare the generated sitemaps with either the sitemap provided by the Web designer or with the intended sitemap of the website and, consequently, iii) plan possible website re-organization. The solution we propose is based on closed frequent sequence extraction and only concentrates on hyperlinks organized in “Web lists”, which are logical lists embedded in the pages. These “Web lists” are typically used for supporting users in Web site navigation and they include menus, navbars and content tables. Experiments performed on three real datasets show that the extracted sitemaps are much more similar to those defined by website curators than those obtained by competitor algorithms.
Radiation eigenmodes of Dicke superradiance

We calculate the field eigenmodes of the superradiant emission from an ensemble of $N$ two-level atoms. While numerical techniques are effective due to the symmetry of the problem, we develop also an analytical method to approximates the modes in the limit of a large number of emitters. We find that Dicke superradiant emission is restricted to a small number of modes, with a little over 90\% of the photons emitted in a single dominant mode.
Bifurcation analysis by particle swarm optimization

This paper explains a bifurcation parameter detection strategy based on particle swarm optimization (PSO), and it shows application examples on detection of local bifurcation parameters appearing in discrete-time dynamical systems and non-autonomous continuous dynamical systems. The algorithm design and analysis part shows that the PSO-based algorithm is fairly simple, easily understandable, and easily implementable. The method requires no careful initialization, exact calculation, gradient information of the system, or Lyapunov exponents. However, the simulation results show that the method accurately detects the local bifurcation parameters regardless of the stability of the periodic point.
An improved noise loss correction algorithm for learning from noisy labels

Despite excellent performance in image classification researches, the training of the deep neural networks (DNN) needs a large set of clean data with accurate annotations. The collection of a dataset is easy, but annotating the collected data is difficult on the contrary. There are many image data on the websites, which contain inaccurate annotations, but trainings on these datasets may make networks easier to over-fit noisy data and cause performance degradation. In this work, we propose an improved joint optimization framework for noise correction, which uses the Combination of Mix-up entropy and Kullback-Leibler entropy (CMKL) as the loss function. The new loss function can achieve better fine-tuning results after updating all label annotations. The experimental results on publicly available CIFAR-10 dataset and Clothing1M dataset show superior performance of our approach compared with other state-of-the-art methods.
k-forrelation optimally separates Quantum and classical query complexity

Aaronson and Ambainis (SICOMP ‘18) showed that any partial function on N bits that can be computed with an advantage δ over a random guess by making q quantum queries, can also be computed classically with an advantage δ/2 by a randomized decision tree making Oq(N1−1/2qδ−2) queries. Moreover, they conjectured the k-Forrelation problem — a partial function that can be computed with q = ⌈ k/2 ⌉ quantum queries — to be a suitable candidate for exhibiting such an extremal separation.
The effectiveness of using augmented reality in teaching geography curriculum on the achievement and attitudes of Omani 10th Grade Students

&lt;p&gt;This study aimed to investigate the effectiveness of using a mobile Augmented Reality&lt;strong&gt; &lt;/strong&gt;application in the geography unit for 10&lt;sup&gt;th&lt;/sup&gt;-grade students on their achievements and attitude. The unit is part of the Omani’s social studies curriculum and entitled, “Environmental Problems and Hazards”. To achieve this purpose, a quasi-experimental study design was conducted. The instruments designed, validated and used to collect data were pre-post achievement tests and attitudinal scale. The participants were 64 male students, divided into two experimental and control groups. The field application period took four weeks in which the control group studied the unit using the normal teaching method, while the mobile Augmented Reality application, HP Reveal, with a website that supports its features, was integrated to teach experimental group. Findings show that there was a statistically significant difference between the pre- and post-field applications in terms of students’ achievement and attitudes. Nevertheless, there was no impact observed when the control and experimental groups’ achievement was compared. The researcher suggested conducting further investigations to examine the value of integrating Augmented Reality in the educational system considering various factors and variables.&lt;/p&gt;
Dense Crowds Detection and Surveillance with Drones using Density Maps

Detecting and Counting people in a human crowd from a moving drone present challenging problems that arise from the constant changing in the image perspective and camera angle.In this paper, we test two different state-of-theart approaches, density map generation with VGG19 trained with the Bayes loss function and detect-then-count with Faster RCNN with ResNet50-FPN as backbone, in order to compare their precision for counting and detecting people in different real scenarios taken from a drone flight.We show empirically that both proposed methodologies perform especially well for detecting and counting people in sparse crowds when the drone is near the ground.Nevertheless, VGG19 provides better precision on both tasks while also being lighter than Faster RCNN.Furthermore, VGG19 outperforms Faster RCNN when dealing with dense crowds, proving to be more robust to scale variations and strong occlusions, being more suitable for surveillance applications using drones.
Language-based translation and prediction of surgical navigation steps for endoscopic wayfinding assistance in minimally invasive surgery

Abstract Purpose In the context of aviation and automotive navigation technology, assistance functions are associated with predictive planning and wayfinding tasks. In endoscopic minimally invasive surgery, however, assistance so far relies primarily on image-based localization and classification. We show that navigation workflows can be described and used for the prediction of navigation steps. Methods A natural description vocabulary for observable anatomical landmarks in endoscopic images was defined to create 3850 navigation workflow sentences from 22 annotated functional endoscopic sinus surgery (FESS) recordings. Resulting FESS navigation workflows showed an imbalanced data distribution with over-represented landmarks in the ethmoidal sinus. A transformer model was trained to predict navigation sentences in sequence-to-sequence tasks. The training was performed with the Adam optimizer and label smoothing in a leave-one-out cross-validation study. The sentences were generated using an adapted beam search algorithm with exponential decay beam rescoring. The transformer model was compared to a standard encoder-decoder-model, as well as HMM and LSTM baseline models. Results The transformer model reached the highest prediction accuracy for navigation steps at 0.53, followed by 0.35 of the LSTM and 0.32 for the standard encoder-decoder-network. With an accuracy of sentence generation of 0.83, the prediction of navigation steps at sentence-level benefits from the additional semantic information. While standard class representation predictions suffer from an imbalanced data distribution, the attention mechanism also considered underrepresented classes reasonably well. Conclusion We implemented a natural language-based prediction method for sentence-level navigation steps in endoscopic surgery. The sentence-level prediction method showed a potential that word relations to navigation tasks can be learned and used for predicting future steps. Further studies are needed to investigate the functionality of path prediction. The prediction approach is a first step in the field of visuo-linguistic navigation assistance for endoscopic minimally invasive surgery.
On the importance of intraindividual variation in nutritional research

Nutritional intervention studies, like those with pre- and probiotics, are often hampered by low effect sizes, reducing the power to demonstrate potential efficacy. Here, we perform computer simulations of a hypothetical clinical trial using such an intervention in order to elucidate determining factors that can be influenced in order to optimise the statistical power. Our simulations demonstrate that steering the study population towards a low intraindividual variation dramatically improves statistical power. A more than 10-fold decrease of number-to-treat could be reached. Also, a careful balancing between the number of subjects and measurements per subject, in combination with possible stratification of the subjects into responders and non-responders, based on inherent intraindividual variation, improves the likelihood to reach statistically significant results. Our results also show that traditional dogmas, with respect to clinical trials, i.e. aiming at low interindividual variation and a high number ( n ) of study participants, should be re-evaluated in favour of reducing intraindividual variation. This reduction in intraindividual variation could be achieved by maintaining a steady lifestyle, including dietary habits among others, within the timeframe of the intervention study.
Dynamic Graph Convolutional Networks Using the Tensor M-Product

Many irregular domains such as social networks, financial transactions, neuron connections, and natural language constructs are represented using graph structures. In recent years, a variety of graph neural networks (GNNs) have been successfully applied for representation learning and prediction on such graphs. In many of the real-world applications, the underlying graph changes over time, however, most of the existing GNNs are inadequate for handling such dynamic graphs. In this paper we propose a novel technique for learning embeddings of dynamic graphs using a tensor algebra framework. Our method extends the popular graph convolutional network (GCN) for learning representations of dynamic graphs using the recently proposed tensor M-product technique. Theoretical results presented establish a connection between the proposed tensor approach and spectral convolution of tensors. The proposed method TM-GCN is consistent with the Message Passing Neural Network (MPNN) framework, accounting for both spatial and temporal message passing. Numerical experiments on real-world datasets demonstrate the performance of the proposed method for edge classification and link prediction tasks on dynamic graphs. We also consider an application related to the COVID-19 pandemic, and show how our method can be used for early detection of infected individuals from contact tracing data.
Influencing Children: Limitations of the Computer-Human-Interactive Persuasive Systems in Developing Societies

The phenomenon of product/business failure, as well as lack of environmental sustainability and learning limitations, is fast becoming a recurrent 'disease' for investors, designers, design sponsors and education policy makers in many developing countries with poor persuasiveness contributing a large quota to such failures.This has greatly hampered the education, poverty alleviation and developmental efforts of the governments of such societies.In a bid to curb this negative trend, children, who are major influencers in product purchase behaviours of adults, have been targeted specifically by persuasive designers, in an effort to both educate and adopt them as means of reaching the larger populace.However, most researches in current persuasive system designs are limited to the information communication/management technology or computerized environments.These systems are technology/internet-driven and many potential users, in reality, in the developing world, unlike the rest of the world is often made to believe, do not have open access to such systems.Unfortunately, the effectiveness of any persuasive system is dependent on its accessibility to its user(s).Technological backwardness (often concealed behind ostentatious self-deceptive facades) has led to the poor persuasiveness of local persuasive systems and products in the third worlds.Therefore, adopting a mixed method for establishing the factor(s) limiting the efficiency of the computer/electronic-human interaction persuasive systems (CHIPS) in South-West Nigeria (N=900), this study established the need to adopt more of the product/entityhuman interaction persuasive system (PEHIPS) as an effective alternative for third world countries as, based on the study outcomes, the CHIPS proved less relatively effective in comparison to PEHIPS in rural regions.It however recommends the alternating adoption of a combination of both computerized and entity/product driven systems for the purpose of optimizing persuasive effectiveness in developing worlds.
Automatic multi-organ segmentation in computed tomography images using hierarchical convolutional neural network.

Purpose: Accurate segmentation of treatment planning computed tomography (CT) images is important for radiation therapy (RT) planning. However, low soft tissue contrast in CT makes the segmentation task challenging. We propose a two-step hierarchical convolutional neural network (CNN) segmentation strategy to automatically segment multiple organs from CT. Approach: The first step generates a coarse segmentation from which organ-specific regions of interest (ROIs) are produced. The second step produces detailed segmentation of each organ. The ROIs are generated using UNet, which automatically identifies the area of each organ and improves computational efficiency by eliminating irrelevant background information. For the fine segmentation step, we combined UNet with a generative adversarial network. The generator is designed as a UNet that is trained to segment organ structures and the discriminator is a fully convolutional network, which distinguishes whether the segmentation is real or generator-predicted, thus improving the segmentation accuracy. We validated the proposed method on male pelvic and head and neck (H&N) CTs used for RT planning of prostate and H&N cancer, respectively. For the pelvic structure segmentation, the network was trained to segment the prostate, bladder, and rectum. For H&N, the network was trained to segment the parotid glands (PG) and submandibular glands (SMG). Results: The trained segmentation networks were tested on 15 pelvic and 20 H&N independent datasets. The H&N segmentation network was also tested on a public domain dataset ( N=38 ) and showed similar performance. The average dice similarity coefficients ( mean±SD ) of pelvic structures are 0.91±0.05 (prostate), 0.95±0.06 (bladder), 0.90±0.09 (rectum), and H&N structures are 0.87±0.04 (PG) and 0.86±0.05 (SMG). The segmentation for each CT takes <10 s on average. Conclusions: Experimental results demonstrate that the proposed method can produce fast, accurate, and reproducible segmentation of multiple organs of different sizes and shapes and show its potential to be applicable to different disease sites.
Named Entity Recognition and Relation Extraction Using Enhanced Table Filling by Contextualized Representations

In this study, we propose a method designed to extract named entities and relations from unstructured text based on table representations. To extract named entities, the proposed method computes representations for entity mentions and long-range dependencies using contextualized representations without hand-crafted features or complex neural network architectures. To extract relations, it applies a tensor dot product to predict all relation labels simultaneously without considering dependencies among relation labels. These advancements significantly simplify the proposed model and the associated algorithm for the extraction of named entities and relations. Despite its simplicity, the experimental results demonstrate that the proposed approach outperformed the state of the-art methods on multiple datasets. Compared with existing table-filling approaches, the proposed method achieved high performance solely by independently predicting the relation labels. In addition, we found that incorporating dependencies of relation labels into the system obtained little performance gain, indicating the effectiveness and sufficiency of the tensor dot-product mechanism for relation extraction in the proposed architecture. Experimental analyses were also performed to explore the benefits of joint training with named entity recognition in relation extraction in our design. We concluded that joint training with named entity recognition assists relation extraction to improve the span-level representation of entities.
Bi-criteria Acceleration Level Obstacle Avoidance of Redundant Manipulator

In this paper, an improved obstacle-avoidance-scheme-based kinematic control problem in acceleration level for a redundant robot manipulator is investigated. Specifically, the manipulator and obstacle are abstracted as mathematical geometries, based on the vector relationship between geometric elements, and the Cartesian coordinate of the nearest point to an obstacle on a manipulator can be found. The distance between the manipulator and an obstacle is described as the point-to-point distance, and the collision avoidance strategy is formulated as an inequality. To avoid the joint drift phenomenon of the manipulator, bi-criteria performance indices integrating joint-acceleration-norm minimization and repetitive motion planning is adopted by assigning a weighing factor. From the perspective of optimization, therefore, an acceleration level quadratic programming (QP) problem is eventually formulated. Considering the physical structure of robot manipulators, inherent joint angle, speed, and acceleration limits are also incorporated. To solve the resultant QP minimization problem, a recurrent neural network based neural dynamic solver is proposed. Then, simulation experiments performing on a four-link planar manipulator validate the feasibility and effectiveness of the proposed scheme.
Person Detection for an Orthogonally Placed Monocular Camera

Counting of passengers entering and exiting means of transport is one of the basic functionalities of passenger flow monitoring systems. Exact numbers of passengers are important in areas such as public transport surveillance, passenger flow prediction, transport planning, and transport vehicle load monitoring. To allow mass utilization of passenger flow monitoring systems, their cost must be low. As the overall price is mainly given by prices of the used sensor and processing unit, we propose the utilization of a visible spectrum camera and data processing algorithms of low time complexity to ensure a low price of the final product. To guarantee the anonymity of passengers, we suggest orthogonal scanning of a scene. As the precision of the counting is relevantly influenced by the precision of passenger recognition, we focus on the development of an appropriate recognition method. We present two opposite approaches which can be used for the passenger recognition in means of transport with and without entrance steps, or with split level flooring. The first approach is the utilization of an appropriate convolutional neural network (ConvNet), which is currently the prevailing approach in computer vision. The second approach is the utilization of histograms of oriented gradients (HOG) features in combination with a support vector machine classifier. This approach is a representative of classical methods. We study both approaches in terms of practical applications, where real-time processing of data is one of the basic assumptions. Specifically, we examine classification performance and time complexity of the approaches for various topologies and settings, respectively. For this purpose, we form and make publicly available a large-scale, class-balanced dataset of labelled RGB images. We demonstrate that, compared to ConvNets, the HOG-based passenger recognition is more suitable for practical applications. For an appropriate setting, it defeats the ConvNets in terms of time complexity while keeping excellent classification performance. To allow verification of theoretical findings, we construct an engineering prototype of the system.
Application of ICT tools in teaching American English for computer science students in the context of global challenges

Abstract The article deals with the urgent issue of American English learning for IT-professionals under challenging conditions of the changeable economic situation in the world. Some statistical data on global education trends and its analysis are given to confirm the topicality of the problem. State-of-the-art trends in foreign language teaching in the context of global challenges are presented. It is stated that informal education environments, distant learning platforms, virtual reality environments, artificial intelligence applications and collaboration platforms are to be mastered by Ukrainian undergraduates. Certain peculiarities of choosing and studying British and American English course by students of IT-specialities at three Ukrainian universities are given: the course choice procedure, the reasons for course preference, and the syllabus content. The best technologies, applications and tools for classroom activities and independent learning are substantiated, specific examples of their application for British and American English course learning are presented. The transferability of the skills inculcated in the course participants through implementation of the certain tools is founded.