Reviewerly/exHarmony huggingface dataset:
Fitting multivariate Erlang mixtures to data: A roughness penalty approach

The class of multivariate Erlang mixtures with common scale parameter has many desirable properties and has widely been used in insurance loss modeling. The parameters of a multivariate Erlang mixture are normally estimated using an expectation–maximization (EM) algorithm as shown in Lee and Lin (2012) and Verbelen et al. (2016). However, when fitting the mixture to data of high dimension, the fitted density surface is often not smooth (with deep peaks and valleys) and the tail fitting may also be rather unsatisfactory. In this paper, we propose a generalized expectation conditional maximization (GECM) algorithm that maximizes a penalized likelihood with a proposed roughness penalty. The roughness penalty is based on integrated squared second derivative of the density function of aggregate data, which is used in functional data analysis. We illustrate the performance of the proposed method through some numerical experiments and real data applications.
Surround sound spreads visual attention and increases cognitive effort in immersive media reproductions

The goal of this study was to explore the effects of different spatial sound configurations on visual attention and cognitive effort in an immersive environment. For that purpose, different groups of people were exposed to the same immersive video, but with different soundtrack conditions: mono, stereo, 5.1 and 7.4.1. The different sound conditions consisted of different artistic adaptations of the same soundtrack. During the visualization of the video, participants wore an eye-tracking device and were asked to perform a counting task. Gaze direction and pupil dilation metrics were obtained, as measures of attention and cognitive effort. Results demonstrate that the conditions 5.1 and 7.4.1 were associated with larger distributions of the visual attention, with subjects spending more time gazing at task-irrelevant areas on the screen. The sound condition which led to more concentrated attention on the task-relevant area was mono. The wider the spatial sound configuration, the greater the gaze distribution. Conditions 7.4.1 and 5.1 were also associated with larger pupil dilations than the mono and stereo conditions, showing that these conditions might lead to increased cognitive demand and therefore increased task difficulty. We conclude that sound design should be carefully planned to prevent visual distraction. More surrounding spatialized sounds may lead to more distraction and more difficulty in following audiovisual contents than less distributed sounds. We propose that sound spatialization and soundtrack design should be adapted to the audiovisual content and the task at hand, varying in immersiveness accordingly.
How Virtual Reality Is Changing the Future of Learning in K-12 and Beyond

The integration of digital tools into alternative education strategies presents a potentially rich area of practical research. Non-traditional learning opportunities are multiplying and gaining widespread implementation due to their efficacy in fostering student engagement with course materials. Immersion tools such as Virtual Reality (VR) educational experiences have received significant attention as potential replacements or complements to existing multimedia educational tools given their increasing affordability and demonstrated value in fostering greater soft skills in students, such as critical thinking and community engagement. In light of the practical value of leveraging VR for alternative education, this study investigates five sampled VR experiences to determine their potential usefulness as teaching aids. Using the Needs, Affordances, and Features (NAF) framework, this research compares and contrasts five fully-immersive VR programs and analyzes the intersection of Needs, Affordances, and Features in each to determine their value in addressing gaps in current alternative education platforms. This study finds that fully immersive VR educational features enable multiple affordances for Teacher Users, Student Users, and both that fulfill specific experiential needs for each group. As such, these VR programs do address needs left unmet by traditional educational tools and underfunded institutions. Further, the inclusion of fully immersive VR tools in educational plans may help address gaps in content creation, distribution, and assessment that are presented by current alternative educational strategies. Therefore, a NAF framework might be implemented for use by VR developers, education professionals, and researchers to determine the potential of a given VR experience for meeting the needs of students and teachers pursuing alternative education strategies.
Advanced Passive Operating System Fingerprinting Using Machine Learning and Deep Learning

Securing and managing large, complex enterprise network infrastructure requires capturing and analyzing network traffic traces in real-time. An accurate passive Operating System (OS) fingerprinting plays a critical role in effective network management and cybersecurity protection. Passive fingerprinting doesn't send probes that introduce extra load to the network and hence it has a clear advantage over active fingerprinting since it also reduces the risk of triggering false alarms. This paper proposes and evaluates an advanced classification approach to passive OS fingerprinting by leveraging state-of-the-art classical machine learning and deep learning techniques. Our controlled experiments on benchmark data, emulated and realistic traffic is performed using two approaches. Through an Oracle-based machine learning approach, we found that the underlying TCP variant is an important feature for predicting the remote OS. Based on this observation, we develop a sophisticated tool for OS fingerprinting that first predicts the TCP flavor using passive traffic traces and then uses this prediction as an input feature for another machine learning algorithm for predicting the remote OS from passive measurements. This paper takes the passive fingerprinting problem one step further by introducing the underlying predicted TCP variant as a distinguishing feature. In terms of accuracy, we empirically demonstrate that accurately predicting the TCP variant has the potential to boost the evaluation performance from 84% to 94% on average across all our validation scenarios and across different types of traffic sources. We also demonstrate a practical example of this potential, by increasing the performance to 91.3% on average using a tool for TCP variant prediction in an emulated setting. To the best of our knowledge, this is the first study that explores the potential for using the knowledge of the TCP variant to significantly boost the accuracy of passive OS fingerprinting.
Rational functions with small value set

Let q be a prime power, and let Fq be the finite field with q elements. In connection with Galois theory and algebraic curves, this paper investigates rational functions h(x)=f(x)/g(x)∈Fq(x) for which the value sets Vh={h(α)|α∈Fq∪{∞}} are relatively small. In particular, under certain circumstances, it proves that h(x) having a small value set is equivalent to the field extension Fq(x)/Fq(h(x)) being Galois.
THE IMPLEMENTATION EXPERIENCE OF STUDENTS MOBILE TRAINING IN THE FUNDAMENTALS OF LEADERSHIP AS A RESOURCE-ORIENTED FORM

In the article, the authors consider mobile learning as a form of resource-based learning of university students in the study of professionally-oriented disciplines. The advantages of mobile learning are revealed: using the latest technology in training; using lightweight, compact, portable devices in training; delivering blended, resource-oriented learning; providing a quality support for training in any format; teaching students with special needs; reducing the costs of educational support; using the new ways of educational content developing; providing the continuous targeted support for the educational process; creating a fun, exciting and comfortable learning experience. The conditions necessary for organization of mobile education in the educational environment of a modern university are described: availability of mobile devices for students and teachers; access to developed electronic educational resources (electronic textbooks and manuals, educational websites, distance learning courses); organization of feedback between the teacher and the student. On the example of the discipline "Fundamentals of Leadership" at the University of Ukoopspilks "Poltava University of Economics and Trade" (PUET), Ukraine, the authors propose ways of organizing mobile learning for distance students. The developed distance course (via distance learning platform Moodle) and the electronic manual on "Fundamentals of Leadership" for mobile devices (program Dr.Explain) are offered. The opportunities and benefits of Dr.Explain program, which allows you to create e-learning manuals for mobile learning, are demonstrated. The article states that Dr.Explain is a convenient and effective tool / aid for developing a variety of electronic educational resources, ranging from electronic lectures and manuals to complete electronic courseware package and educational Internet resources. The authors emphasize that for mobile learning, it is necessary to create electronic teaching aids in the formats supported by mobile devices.
Automatic Image Labeling with Click Supervision on Aerial Images

Manually generating annotated bounding boxes for object detection is time consuming. Although human-annotation is the most accurate approach, machine learning models can provide additional assistance. In this paper, we propose a human in a loop automatic image labeling framework focusing on aerial images with less features for detection. The proposed model consists of two main parts, prediction model and adjustment model. The user first provides click location to prediction model to generate a bounding box of a specific object. The bounding box is then fine-tuned by the adjustment model for more accurate size and location. A feedback and retrain mechanism is implemented that allows the users to manually adjust the generated bounding box and provide feedback to incrementally train the adjustment network during runtime. This unique online learning feature enables user to generalize existing model to target classes not initially presented in the training set, and gradually improves the specificity of the model to those new targets online. We demonstrate promising results on Neovision 2 Heli dataset. Compared to the state-of-the-art method, our prediction model achieves a higher detection rate, and our adjustment model improves the IOU by up to 45%.
Developmental Learning of Value Functions in a Motivational System for Cognitive Robotics

Motivation is quite an important topic when addressing continual open-ended learning processes in autonomous robots. The three main issues that need to be considered are, firstly, how does a designer define what the robot strives for in a manner that is independent from any particular domain it may find itself in. Secondly, once that robot is in a domain, how does it go about finding and relating goals in that particular domain on its own. Finally, the third issue is, once a goal is found, how does a robot establish a representation, usually in the form of a Value Function, that will allow it to exploit that goal. This paper deals with the third issue in the framework of the motivational engine we have designed for cognitive architectures. It addresses the problem of efficiently and appropriately learning complex Value Functions starting from intrinsically motivated traces of valuated robot actions that are often ambiguous and multivalued. To this end, a developmental learning mechanism is proposed that relies on the concurrent application of a real time ANN learning procedure over the traces of the valuated robot actions, and a simpler sensor correlation-based approach to allow for the production of better configured data traces for the learning process. The mechanism is analyzed and discussed over an experiment considering a real Baxter robot.
Gradient Deconfliction-Based Training For Multi-Exit Architectures

Muiti-exit architectures, in which a sequence of intermediate classifiers are introduced at different depths of the feature layers, perform adaptive computation by early exiting “easy” samples to speed up the inference. In this paper, we propose a new gradient deconfliction-based training technique for multi-exit architectures. In particular, the conflicting between the gradients back-propagated from different classifiers is removed by projecting the gradient from one classifier onto the normal plane of the gradient from the other classifier. Experiments on CFAR-100 and ImageNet show that the gradient deconfliction-based training strategy significantly improves the performance of the state-of-the-art multi-exit neural networks. Moreover, this method does not require within architecture modifications and can be effectively combined with other previously-proposed training techniques and further boosts the performance.
Conditional Generative Adversarial Network for Predicting 3D Medical Images Affected by Alzheimer’s Diseases

Predicting the evolution of Alzheimer’s disease (AD) is important for accurate diagnosis and the development of personalized treatments. However, learning a predictive model is challenging since it is difficult to obtain a large amount of data that includes changes over a long period of time. Conditional Generative Adversarial Networks (cGAN) may be an effective way to generate images that match specific conditions, but they are impractical to generate 3D images due to memory resource limitations. To address this issue, we propose a novel cGAN that is capable of synthesizing MR images at different stages of AD (i.e., normal, mild cognitive impairment, and AD). The proposed method consists of a 2D generator that synthesizes an image according to a condition with the help of 2D and 3D discriminators that evaluate how realistic the synthetic image is. We optimize both the 2D GAN loss and the 3D GAN loss to determine whether multiple consecutive 2D images generated in a mini-batch have real or fake appearance in 3D space. The proposed method can generate smooth and natural 3D images at different conditions by using a single network without large memory requirements. Experimental results show that the proposed method can generate better quality 3D MR images than 2D or 3D cGAN and can also boost the classification performance when the synthesized images are used to train a classification model.
Unsupervised Learning-Based Nonrigid Registration of High Resolution Histology Images

The use of different dyes during histological sample preparation reveals distinct tissue properties and may improve the diagnosis. Nonetheless, the staining process deforms the tissue slides and registration is necessary before further processing. The importance of this problem led to organizing an open challenge named Automatic Non-rigid Histological Image Registration Challenge (ANHIR), organized jointly with the IEEE ISBI 2019 conference. The challenge organizers provided 481 image pairs and a server-side evaluation platform making it possible to reliably compare the proposed algorithms. The majority of the methods proposed for the challenge were based on the classical, iterative image registration, resulting in high computational load and arguable usefulness in clinical practice due to the long analysis time. In this work, we propose a deep learning-based unsupervised nonrigid registration method, that provides results comparable to the solutions of the best scoring teams, while being significantly faster during the inference. We propose a multi-level, patch-based training and inference scheme that makes it possible to register images of almost any size, up to the highest resolution provided by the challenge organizers. The median target registration error is close to 0.2% of the image diagonal while the average registration time, including the data loading and initial alignment, is below 3 s. We freely release both the training and inference code making the results fully reproducible.
Unpaired Image-To-Image Shape Translation Across Fashion Data

We address the problem of unpaired geometric image-to-image translation. Rather than transferring the style of an image as a whole, our goal is to translate the geometry of an object while preserving its appearance. Our model is trained without the need for paired images. It performs all steps of the shape transfer within a single model and without additional post-processing stages. Experiments on clothing-based datasets show the effectiveness of the proposed method.
Automatic Construction of Dataset with Automatic Annotation for Object Detection

This paper proposes a method for the automatic construction of a dataset with annotation data for object detection. The accuracy of the object detection method depends on the dataset in general. The dataset for object detection needs many images with annotation data. Obtaining image data by manual operation takes a lot of costs. It also costs much that annotation data are made by manual annotation software. This paper tries to solve these problems to construct the image dataset for the object detection automatically. The proposed method uses a method to collect the image data automatically among images on the Web using Web image mining. A new method for making annotation data is also proposed. The Mask R-CNN is used for automatical annotation. The proposed approach constructs a dataset automatically without almost manual operation. It is confirmed that the proposed approach performs the automatic construction of the dataset with high accuracy.
Splitter: An Efficient Scheme to Determine the Geolocation of Cloud Data Publicly

Outsourcing data to the cloud has become a trend, and the geolocation of cloud data attracts public attention in recent years, which is relevant to data availability (e.g., disaster tolerant), data security and policies (e.g. USA Patrio Act). Unfortunately, cloud service providers are not fully trusted to the data owners. This is because the data owners lose the physical control over the cloud data, and cloud service providers have the ability and motivation to change the geolocation of cloud data between different data centers. Therefore, designing a scheme to determine the geolocation of cloud data for data owners is an urgent problem to be solved.In this paper, we propose Splitter, an efficient scheme to determine the geolocation of cloud data publicly. In Splitter, we first design a splitting method, which breaks up the challenge and proof, and only considers the response delay resulting from the general operations (i.e., addition and multiplication) to obtain the accurate response delay. Second, we combine random forest algorithm and improved triangulation method to determine the geolocation accurately. Third, we take a series of theoretical comparison and extensive experiments to evaluate our scheme. The results illustrate the efficiency and practicality of our scheme.
Multi-UAVs Coverage Path Planning

In this literature review, we attempt to summarize and analyze some recent works on Coverage Path Planning (CPP) strategies. We are interested by those that consider UAV fleets. Covering a region of interest is like sweeping a space in either 2D or 3D, and visiting specific points just once each, to cover as much space as possible with obstacle prevention. These strategies are classified according to their evolution trends, such as grid-based methods, evolutionary algorithms, sampling based methods, etc.
A New Personalized POI Recommendation Based on Time-Aware and Social Influence

With the rapid growth of the location-based social networks (LBSNs), Point of Interest (POI) recommendation has become an important research topic in data mining. However, the existing works do not reasonably utilize the time sensitivity of POI recommendations and have not taken full account of the user’s behavior preferences at different time, causing the POI recommendation performance poor. We propose a Time-aware and POI Recommendation model based on Tensor Factorization, named TPR-TF. Firstly, we study the POI recommendation problem of time sensitivity and propose a temporal dynamic segmentation algorithm based on hierarchical clustering. Through dividing the fine grain of time, the experiment result is more reasonable and effectively than the previous method which divided identical time empirically. Secondly, by combining the time-aware recommendation with the influence of the user’s direct friendship and potential friendship, we expand the scope of users’ social influence, and then further improve the POI recommendation performance. Experimental results on the two datasets indicate that our TPR-TF model is superior to the current mainstream POI recommendation models both in precision and recall.
Adaptive Lattice-Aware Image Demosaicking Using Global And Local Information

A novel approach for image demosaicking based on adaptive lattice-aware filter (ALF) and global refinement unit (GRU) is proposed in this work. We generate ALFs dynamically, which are adaptive to positions of pixels within color lattices in a color filter array, to obtain a locally demosaicked image. We then refine the locally demosaicked image using GRU to exploit global information, as well as local information. To extend the receptive fields efficiently, we adopt dilated convolutions in GRU. Experimental results demonstrate that the proposed algorithm provides the state-of-the-art performances in standard demosaicking datasets.
An explainable data-driven approach to web directory taxonomy mapping

The spread of e-commerce and web applications has fostered the integration of cross-domain business activities. To efficiently retrieve products and services, web directories allow customers to browse multiple-level taxonomies to find specific products or services according to a predefined categorization. Providers need to periodically update web directory lists by aligning in-house taxonomies to domain-specific hierarchies coming from external sources. However, such taxonomy mapping procedures are often semi-automatic and rely on traditional word disambiguation techniques to capture the semantics behind categories and products descriptions. Hence, the flexibility and explainability of the underlying models are quite limited. This paper proposes an automated, explainable approach to web directory taxonomy mapping based on text categorization. It exploits two complementary word-based text representations: a frequency-based representation, which captures syntactic text similarities, and an embedding one, which highlights the underlying semantic relationships among words. Since the proposed solution is purely data-driven, it can be successfully applied to business domains where there is a lack of semantic models. The frequency-based text representation has shown to be particularly suitable for driving the automated taxonomy mapping procedure, whereas the embedding space has been profitably used to provide local explanations of the category assignments.