Reviewerly/exHarmony huggingface dataset:
A Comparative Study on Variational Autoencoders and Generative Adversarial Networks

Generative Adversarial Networks (GAN) have been remarkable at generating artificial data, especially realistic looking images. This learning framework has proven itself to be effective in synthetic image generation, semantic image hole filling, semantic image editing, style transfer and many more. On the other hand, variational auto-encoders (VAE) have also been quite effective, so much so that mathematically it is often more accurate at generating images resembling to its original dataset. Nevertheless, images generated by VAE suffer from blurriness and are generally less realistic looking from human perception. In this paper we take a broad view on both systems and propose a theoretical approach to combine them and bring out the best of both.
Screening and early identification of microcalcifications in breast using texture-based ANFIS classification

The objective of this investigation is to widen the robust detection schemes for the detection of breast cancer at an early stage. This is a noninvasive method called the adaptive neurofuzzy structure (ANFIS) to diagnose microcalcifications (cancerous lesions) in the mammary glands. An investigation of breast cancer by using ANFIS along with the clinical inputs from medical practioners is proposed. Outcomes of the discussions from various medical practioners and various algorithms are reviewed. Clinical images from the MIAS databased are used for testing and training of the proposed algorithm. Texture-based entropy values are also used for training and testing of features. The ANFIS-based classification is an efficient method of breast cancer diagnosis. The performance of the proposed (texture features with ANFIS) method is compared with the outputs obtained from the K-means clustering algorithm, wavelet transform, and artificial neural network-based classification using mean absolute deviation as a feature for early stage diagnosis.
Semi-Supervised Image-to-Image Translation

Image-to-image translation is a long-established and a difficult problem in computer vision. In this paper we propose an adversarial based model for image-to-image translation. The regular deep neural-network based methods perform the task of image-to-image translation by comparing gram matrices and using image segmentation which requires human intervention. Our generative adversarial network based model works on a conditional probability approach. This approach makes the image translation independent of any local, global and content or style features. In our approach we use a bidirectional reconstruction model appended with the affine transform factor that helps in conserving the content and photorealism as compared to other models. The advantage of using such an approach is that the image-to-image translation is semi-supervised, independent of image segmentation and inherits the properties of generative adversarial networks tending to produce realistic. This method has proven to produce better results than Multimodal Unsupervised Image-to-image translation.
A Comprehensive Classification of 3D Selection and Manipulation Techniques

The importance of 3D interaction is constantly increasing - not least because of new developments in ubiquitous computing and augmented and virtual reality. Current 3D user interfaces allow for an expressive and accurate input in three-dimensional space. 3D interaction techniques are responsible for mapping the three-dimensional input onto actions within the system and for returning appropriate feedback to the user. For developers of applications using 3D interaction and for designers of new 3D interaction techniques it is important to understand how existing techniques differ and how they can be classified. In this work we summarize existing characterizations of 3D interaction and adapt them to allow a comprehensive classification of 3D interaction techniques for selection and manipulation of virtual objects.
Improving Reliability of Object Detection for Lunar Craters Using Monte Carlo Dropout

In the task of detecting craters on the lunar surface, some craters were difficult to detect correctly, and a Deep Neural Network (DNN) could not represent the uncertainty of such detection. However, a measure of uncertainty could be expressed as the variance of the prediction by using Monte Carlo Dropout Sampling (MC Dropout). Although MC Dropout has often been applied to fully connected layers in a network in recent studies, many convolutional layers are used to recognize the subtle features of a crater in the crater-detecting network. In this paper, we extended the application of MC Dropout to a network having a number of convolutional layers, and also evaluated the methodology of dropping out the convolutional layers. As a result, in the convolutional neural network, we represent the more correct variance by using filter-based dropout and evaluating the uncertainty for each feature map size. The precision of prediction in lunar crater detection was improved by 2.1% by rejecting a prediction result with high variance as a false positive compared with the variance when predicting the training data.
On the Prioritization of Design-Relevant Smelly Elements

Software systems are likely to face what is called design problems. Given the typical lack of design documentation, developers have to rely on implementation-level symptoms, the so-called code smells, to identify and remove design problems. A code smell is a microstructure in the program that can indicate the presence of a design problem. Large programs have hundreds or thousands of program elements (e.g., classes) in which a significant proportion may be affected by smells. Consequently, due to time constraints and the large number of elements, developers have to prioritize the designrelevant program elements, i.e., locate a shortlist of elements that are suspects of having design-relevant smells. However, this task is hard and time-consuming. Unfortunately, the literature fails to provide developers with effective heuristics that automate such prioritization task. The objective of this paper is to propose heuristics that effectively locate a shortlist of design-relevant smelly program elements. For this purpose, we report two studies. In the first one, we investigated the criteria that developers used in practice to accurately prioritize design-relevant smelly elements. Based on these criteria, we derived a preliminary suite of prioritization heuristics. Since we do not know if the heuristics are suitable for an effective prioritization across multiple projects, we performed a second study to evaluate the proposed heuristics. We found that two out of nine heuristics reached an average precision higher than 75% for the four projects we analyzed. Thus, our findings suggest these heuristics are promising to support developers in prioritizing design-relevant smelly elements.
Hierarchical hybrid control for Multiple Mobile Robot Systems

The paper presents a hybrid (discrete-event/continuous-time) control concept for a system of mobile robots sharing a common motion space. The logics underlying its construction formally ensures collision-free and deadlock-free motion of the robots, and the modular structure of the controller allows for its modification and experimental optimization.
Multinomial <i>N</i>‐mixture models for removal sampling

Abstract Multinomial ‐mixture models are commonly used to fit data from a removal sampling protocol. If the mixing distribution is negative binomial, the distribution of the counts does not appear to have been identified, and practitioners approximate the requisite likelihood by placing an upper bound on the embedded infinite sum. In this paper, the distribution which underpins the multinomial ‐mixture model with a negative binomial mixing distribution is shown to belong to the broad class of multivariate negative binomial distributions. Specifically, the likelihood can be expressed in closed form as the product of conditional and marginal likelihoods and the information matrix shown to be block diagonal. As a consequence, the nature of the maximum likelihood estimates of the unknown parameters and their attendant standard errors can be examined and tests of the hypothesis of the Poisson against the negative binomial mixing distribution formulated. In addition, appropriate multinomial ‐mixture models for data sets which include zero site totals can also be constructed. Two illustrative examples are provided.
Arabic Text Lossless Compression by Characters Encoding

The aim of this paper is to suggest a new sequential encoding technique that efficiently converts Arabic characters string from UTF-8 to ANSI characters coding. The encoding algorithm presented in this paper significantly reduces the file size. The decoding method transforms the encoded ANSI string back to its original format. Unlike the one-byte ANSI characters, Arabic alphabets are currently being stored in 2 bytes size which leads to inefficient space utilization. The newly developed sequential encoding technique reduces the space required for storage up to fifty percent. In addition, the proposed technique will retain the Arabic encoded text to its original form after decoding, which is leading to a lossless text compression. Thus, addressing the common concern of the currently available Arabic characters compression techniques.
Progressive Web Application Assessment Using AHP

The development of software applications has now shifted from desktop to mobile devices. Mobile application developers have been facing hardware configuration and network communication constraints in native, hybrid, and mobile web-based software architecture for a long time. Progressive Web Application is new research in this direction, created in order to overcome the limitations of native, hybrid, and mobile web-based application architecture for mobile devices. The Progressive Web Application architecture consists of a single software application which runs on multiple operating systems (android, iOS, and window phones) efficiently, uses device memory, and is even accessible in the event of no internet connection. Many IT companies have reported overall improvement in their mobile application user base after switching to Progressive Web Application. The recent discussions about Progressive Web Application on the google platform predict a demanding future. Hence, this article seeks to investigate the validity of claims that the Progressive Web Application has the edge over native, hybrid, and mobile web application architecture. The study uses the Analytical Hierarchy Process for deciding which of the architecture types suites the development of a mobile application based on three established constraints. The study outcome will aid software development communities in comprehending the Progressive Web Application and motivate them to use it in the development of an optimized mobile application.
Improved Rectangle Template Matching Based Feature Point Matching Algorithm

Feature point matching is an essential section in image matching. Traditional feature point matching algorithm uses merely the information of feature points such as the descriptors, and on some occasions this may lead to a loss of feature points or even mismatches. This paper proposed an improved feature point matching algorithm based on rectangle template matching. Provided with 2-dimensional coordinates of feature points, a novel parameter named "inner-point index" is calculated and used to extract templates from the input image. On the basis of template matching between the input image and the other image, the process of feature point matching is carried out between templates and the matched areas, using Brute Force matching algorithm. Experimental results show that the proposed algorithm can obtain more correct feature point matches, thus enhances the accuracy of feature point matching.
Multi-stage adaptive regression for online activity recognition

Online activity recognition which aims to detect and recognize activity instantly from a continuous video stream is a key technology in human-robot interaction. However, the partial activity observation problem, mainly due to the incomplete sequence acquisition, makes it greatly challenging. This paper proposes a novel approach, named Multi-stage Adaptive Regression (MAR), for online activity recognition with the main focus on addressing the partial observation problem. Specifically, the MAR framework delicately assembles overlapped activity observations to improve its robustness against arbitrary activity segments. Then multiple score functions corresponding to each specific performance stage are collaboratively learned via a adaptive label strategy to enhance its power of discriminating similar partial activities. Moreover, the Online Human Interaction (OHI) database is constructed to evaluate the online activity recognition in human interaction scenarios. Extensive experimental evaluations on the Multi-Modal Action Detection (MAD) database and the OHI database show that the MAR method achieves an outstanding performance over the state-of-the-art approaches.
Teachyverse

Over the last decades, E-learning has gained a lot of popularity and enabled students to learn in front of their computers using Internet-based learning systems rather than physically attending lectures. Those E-learning systems are different from traditional learning and do not fully immerse the student in the learning environment. Thus, we propose Teachyverse, an immersive VR lecture hall that combines e-learning, traditional learning, and remote collaboration. Teachyverse immerses the student in a virtual lecture hall. A proof-of-concept study shows that students perceive lectures in Teachyverse as fun and would like to use Teachyverse as a further E-Learning option.
Towards Logical Specification of Statistical Machine Learning

We introduce a logical approach to formalizing statistical properties of machine learning. Specifically, we propose a formal model for statistical classification based on a Kripke model, and formalize various notions of classification performance, robustness, and fairness of classifiers by using epistemic logic. Then we show some relationships among properties of classifiers and those between classification performance and robustness, which suggests robustness-related properties that have not been formalized in the literature as far as we know. To formalize fairness properties, we define a notion of counterfactual knowledge and show techniques to formalize conditional indistinguishability by using counterfactual epistemic operators. As far as we know, this is the first work that uses logical formulas to express statistical properties of machine learning, and that provides epistemic (resp. counterfactually epistemic) views on robustness (resp. fairness) of classifiers.
A Knowledge Graph based Disaster Storyline Generation Framework

Web medium service has emerged to be a dominant way for billions of individuals sharing and spreading instant news and information. Meanwhile, the challenging issues for delivering high-quality disaster related information are also brought up for the information overload, fast response and dynamic of disaster events. In this paper, we explore the problem of generating storyline from huge amount of web information and propose a knowledge graph-based disaster storyline generating framework. The deep learning technique and the semi-supervision method are first used to extract two kinds of triples from disaster news. Then the evolution of disasters is summarized in a way of graph-based which can clearly show the sketch of disaster development. Finally, the location entities are extracted by the name entity recognized model. Compared with commonly used text summarization storyline, the proposed framework can provide a better user situational awareness and deeper understanding on disaster events by the presentation of graph.
Electrooculography dataset for reading detection in the wild

Because of the diversity of document layouts and reading styles, detecting reading activities in real life is a challenging task compared to the detection in the laboratory setting. For contributing to the implementation of robust reading detection algorithms, we introduce a dataset which contains 220 hours of sensor signals from JINS MEME electrooculography glasses and corresponding ground truth activity labels. As a baseline study, we propose a statistical feature based reading detection approach and evaluate it on the dataset.
Distributed Multitarget Tracking Based on Diffusion Strategies Over Sensor Networks

We consider the distributed multitarget tracking over sensor networks, where each node only communicates with its neighbors.We develop a diffusion-based distributed multisensor multitarget tracking algorithm.The state update of the diffusion-based distributed algorithm is mainly composed of two phases: an adaptation phase and a combination phase.During the adaptation phase, each node updates its local estimate by using all its neighbors' measurements.It is achieved based on a multi-sensor cardinalized probability hypothesis density filter.During the combination phase, each node fuses all its neighbors' local estimates.It is achieved based on a generalized version of covariance intersection technique.Compared to the consensusbased distributed algorithm, the proposed algorithm has two advantages.First, it can provide more accurate and robust tracking results, especially when the detection probability that the sensors detect the targets is low.Second, it has lower communication load because the consensus iterations are not required.Numerical results are provided to illustrate the performance of the proposed algorithm.
InterCriteria Analysis with Interval-Valued Intuitionistic Fuzzy Evaluations

The Intercriteria Analysis (ICA) is a new tool for decision making similar, but different from the correlation analyses. In the present paper, a new form of ICA, based over interval-valued intuitionistic fuzzy evaluations is described for a first time.
Modelling Learners’ Behaviour: A Novel Approach Using GARCH with Multimodal Data

Most of the contemporary approaches in learner behaviour modelling either quantize continuous data into discrete states/events (e.g., HMM), or assume that the patterns in the data are distributed homogeneously in time (e.g., auto-regression). This paper proposes a novel approach that overcomes the above mentioned issues and models learner behaviour using Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH). GARCH uses continuous time-series data, without the need for information quantization, and it considers the heterogeneity of event distribution in the time-series. A study was conducted to demonstrate how GARCH can be configured in an adaptive assessment setting. Specifically, GARCH was applied on six different constructs from eye-tracking and electroencephalogram (EEG) data, and compared with existing methods of modelling time-series data, such as Markov Models and models having auto-regressive components. The comparison shows that the models having a GARCH component outperform other models for most of the students, for all the variables considered. The results are encouraging towards building accurate learner behaviour models, adequate to drive the design and development of adaptive feedback tools (e.g., an early alert system), but further investigation is required.
Controlling the Bias-Variance Tradeoff via Coherent Risk for Robust Learning with Kernels

In supervised learning, we learn a statistical model by minimizing a measure of fitness averaged over data. Doing so, however, ignores the variance, i.e., the gap between the optimal within a hypothesized function class and the Bayes Risk. We propose to account for both the bias and variance by modifying training to incorporate coherent risk which quantifies the uncertainty of a given decision. We develop the first online solution to this problem when estimators belong to a reproducing kernel Hilbert space (RKHS), which we call Compositional Online Learning with Kernels (COLK). COLK addresses the fact that (i) minimizing risk functions requires handling objectives which are compositions of expected value problems by generalizing the two time-scale stochastic quasi-gradient method to RKHSs; and (ii) the RKHS-induced parameterization has complexity which is proportional to the iteration index which is mitigated through greedily constructed subspace projections. We establish linear convergence in mean to a neighborhood with constant stepsizes, as well as the fact that its complexity is at-worst finite. Experiments on synthetic and benchmark data demonstrate that COLK exhibits consistent performance across training runs, estimates that are both low bias and low variance, and thus marks a step towards overcoming overfitting.
The Asymptotic Complexity of Coded-BKW with Sieving Using Increasing Reduction Factors

The Learning with Errors problem (LWE) is one of the main candidates for post-quantum cryptography. At Asiacrypt 2017, coded-BKW with sieving, an algorithm combining the Blum-Kalai-Wasserman algorithm (BKW) with lattice sieving techniques, was proposed. In this paper, we improve that algorithm by using different reduction factors in different steps of the sieving part of the algorithm. In the Regev setting, where q = n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and σ = n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.5</sup> /(√2π log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> 2 n), the asymptotic complexity is 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.8917n</sup> , improving the previously best complexity of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.8927n</sup> . When a quantum computer is assumed or the number of samples is limited, we get a similar level of improvement.
A Recognition Method for Hand Motion Direction Based on Charge Induction

To develop a cost-efficient technique of hand control, one novel method based on charge induction is proposed to recognize hand motion direction by applying three charge/potential electrodes. Firstly, the theoretical derivation of charge detection for moving target is introduced and experiment verification of hand motion is given. Then, the direction recognition model is established and the accuracy analysis is illustrated, which is found that the detection error is decreased with the decrease of the time measurement error and the motion velocity. Finally, the experiments for hand motion direction recognition are carried out under conditions of full occlusion, darkness and body motion interference. Without the need of wearing device on the hand, the proposed method can achieve non-contact recognition for hand motion direction under the conditions of full occlusion and darkness, which is one outstanding characteristic, especially for control applications like in smart home.
Weakly Supervised Domain Detection

In this paper we introduce domain detection as a new natural language processing task. We argue that the ability to detect textual segments that are domain-heavy (i.e., sentences or phrases that are representative of and provide evidence for a given domain) could enhance the robustness and portability of various text classification applications. We propose an encoder-detector framework for domain detection and bootstrap classifiers with multiple instance learning. The model is hierarchically organized and suited to multilabel classification. We demonstrate that despite learning with minimal supervision, our model can be applied to text spans of different granularities, languages, and genres. We also showcase the potential of domain detection for text summarization.