Reviewerly/exHarmony huggingface dataset:
DESeeker: Detecting Epistatic Interactions Using a Two-Stage Differential Evolution Algorithm

Epistatic interactions are generally defined as the interactions between different single-nucleotide polymorphisms (SNPs). Identifying epistatic interactions is important for determining the individual susceptibility of complex diseases. In large-scale association studies, finding epistatic interactions in the large volume of SNP data is a challenging issue. Since the current search approaches are confronted with the serious problem of computational burden, developing an efficient algorithm for dealing with the intensive computing problem would be significant. In this paper, a novel differential evolution-based algorithm DEseeker is proposed to detect epistatic interactions. DEseeker, combined with a local search and a self-adapting parameter tuning strategy, employs a two-stage design of DE to enhance its search capability. DEseeker is compared with the other recent algorithms on a set of simulated datasets and a real biological dataset. The experimental results on the simulated datasets show that the proposed algorithm is superior to the other compared algorithms in terms of detection power. The discovery of the real biological dataset demonstrates that the proposed algorithm is promising for practical disease prognosis.
Data Augmentation Computing Model Based on Generative Adversarial Network

The edge intelligent computing technology can reduce the delay and energy consumption of deep learning model reasoning through the collaborative terminal acquisition equipment and edge server. We apply the neural network to the edge computing and build a data augmentation computing model based on the sparse data volume. Then, we get an intelligent generative image after the network training to achieve the effect of enhancement computing. In this paper, we choose a relatively small number of national elements and the generative adversarial network (GAN) as the experimental data calculation set and network model. First, we normalize the preprocessing of the collected data to form the initial sample data set. Second, the model extracts the feature vector by input image to the convolution neural network (CNN) layer. After that, we use a random noise vector z which follows a Gaussian distribution as the initial input of the conditional generative adversarial network (CGAN). The feature vector extracted from the image is taken as a label and a condition constraint of the CGAN to train the parameters of the CGAN. Finally, the trained CGAN model is used to complete the data augmentation computing. A total of 350 samples were collected, and 97 sample images were actually applied for data augmentation. The enhanced data set of this model is as many as 1,700 samples, and it is found that the generated image is of good quality by using this data set for the peak signal-to-noise ratio (PSNR) detection, which is of innovative value in the standard of real samples.
Human Re-Identification with a Robot Thermal Camera Using Entropy-Based Sampling

Human re-identification is an important feature of domestic service robots, in particular for elderly monitoring and assistance, because it allows them to perform personalized tasks and human-robot interactions. However vision-based re- identification systems are subject to limitations due to human pose and poor lighting conditions. This paper presents a new re-identification method for service robots using thermal images. In robotic applications, as the number and size of thermal datasets is limited, it is hard to use approaches that require huge amount of training samples. We propose a re-identification system that can work using only a small amount of data. During training, we perform entropy-based sampling to obtain a thermal dictionary for each person. Then, a symbolic representation is produced by converting each video into sequences of dictionary elements. Finally, we train a classifier using this symbolic representation and geometric distribution within the new representation domain. The experiments are performed on a new thermal dataset for human re-identification, which includes various situations of human motion, poses and occlusion, and which is made publicly available for research purposes. The proposed approach has been tested on this dataset and its improvements over standard approaches have been demonstrated.
Continuous Transparent Mobile Device Touchscreen Soft Keyboard Biometric Authentication

Mobile banking, shopping, and in-app purchases utilize persistent authentication states for access to sensitive data. One-shot authentication permits access for a fixed time period. For instance, a [user name, password] based authentication allows a user access to all the shopping and payments data in the Amazon shopping app. Traditional user passwords and lock screens are easily compromised. Snooping attacks - observing an unsuspecting user entering passwords - and Smudge attacks - examining touchscreen finger oil residue - enable compromised user authentication. Mobile device interactions provide robust human and device identity data. Such biometrics enhance authentication. Behavioral attributes during information input constitute the password. Adversary password reproduction difficulty increases since pure observation is insufficient. Current mobile continuous authentication schemes use, among others, touchscreen swipe interactions or keyboard input timing. Many of these methods require cumbersome training or intrusive authentication. Software keyboard interactions provide a consistent biometric data stream. We develop biometric profiles using touch pressure, location, and timing. New interactions authenticate against a profile using a distance metric. Classification achieves 100% accuracy in 3840.33 milliseconds on Nexus 7 tablets.
An analysis of altitude, citizen science and a convolutional neural network feedback loop on object detection in Unmanned Aerial Systems

Using automated processes to detect wildlife in uncontrolled outdoor imagery in the field of wildlife ecology is a challenging task. In imagery provided by Unmanned Aerial Systems (UAS), this is especially true where individuals are small and visually similar to background substrates. To address these challenges, this work presents an automated feedback loop which can operate on large scale imagery, such as UAS generated orthomosaics, to train convolutional neural networks (CNNs) with extremely unbalanced class sizes. This feedback loop was used to help train CNNs using imagery classified by both expert biologists and citizen scientists at the [email protected] project. Utilizing the feedback loop dramatically reduced population count error rates from previously published work: from +150% to −3.93% on citizen scientist training data and +88% to +5.24% on expert training data. The system developed was then utilized to investigate the effect of altitude on CNN predictions. The training dataset was split into three subsets depending on the altitude of the imagery (75 m, 100 m and 120 m). While the lowest altitude was shown to provide the best predictions of the three (+11.46%), the aggregate data set still provided the best results (−3.93%) indicating that there is greater benefit to be gained from a large data set at this scale, and there is potential benefit to having training data from multiple altitudes. This article is an extended version of “Detecting Wildlife in Unmanned Aerial Systems Imagery using Convolutional Neural Networks Trained with an Automated Feedback Loop” published in the proceedings of the 18th International Conference of Computational Science (ICCS 2018) [1].
Non-Gaussian Filter for Continuous-Discrete Models

We propose a new particle filter for nonlinear continuous-discrete models. The proposed filter is based on the multiple distribution estimation with a bank of extended Kalman-Bucy filters. Compared to the simple application of a particle filter, i.e., the bootstrap filter (Monte Carlo filter), to the continuous-discrete models, the proposed filter retains superior integration and fewer particle impoverishment properties. The performance of the proposed filter is also verified using the benchmark simulation model of satellite re-entry.
Scalable Knowledge Graph Construction over Text using Deep Learning based Predicate Mapping

Automatic extraction of information from text and its transformation into a structured format is an important goal in both Semantic Web Research and computational linguistics. Knowledge Graphs (KG) serve as an intuitive way to provide structure to unstructured text. A fact in a KG is expressed in the form of a triple which captures entities and their interrelationships (predicates). Multiple triples extracted from text can be semantically identical but they may have a vocabulary gap which could lead to an explosion in the number of redundant triples. Hence, to get rid of this vocabulary gap, there is a need to map triples to a homogeneous namespace. In this work, we present an end-to-end KG construction system, which identifies and extracts entities and relationships from text and maps them to the homogenous DBpedia namespace. For Predicate Mapping, we propose a Deep Learning architecture to model semantic similarity. This mapping step is computation heavy, owing to the large number of triples in DBpedia. We identify and prune unnecessary comparisons to make this step scalable. Our experiments show that the proposed approach is able to construct a richer KG at a significantly lower computation cost with respect to previous work.
Universal Tree Source Coding Using Grammar-Based Compression

The problem of universal source coding for binary trees is considered. Zhang, Yang, and Kieffer derived upper bounds on the average-case redundancy of codes based on directed acyclic graph (DAG) compression for binary tree sources with certain properties. In this paper, a natural class of binary tree sources is presented such that the demanded properties are fulfilled. Moreover, for both subclasses considered in the paper of Zhang, Yang, and Kieffer, their result is improved by deriving bounds on the maximal pointwise redundancy (or worst-case redundancy) instead of the average-case redundancy. Finally, using context-free tree grammars instead of DAGs, upper bounds on the maximal pointwise redundancy for certain binary tree sources are derived. This yields universal codes for new classes of binary tree sources.
Digital twins as a resource for design research

IoT products are embedded with sensors that transmit live data about their use and environment. A key challenge for designers is to gather useful insights from this data in order to accelerate product research, which can be time consuming and labour intensive. Through the Chatty Products dashboard we aim to explore how virtual representations of IoT products and their sensor data, also known as digital twins, can support insight gathering. This demo will present a series of Bluetooth IoT speakers, which are connected to the Chatty Products dashboard, a data exploration and visualisation research tool containing supervisory digital twins of the speakers. The project aims to visualise live data as it relates to the physical product in the wild, enabling contextual inquiry and supporting data exploration. The demo will promote a dialogue around how digital twins can be used to gather design insights based on live data.
Model-Based Separation, Detection, and Classification of Eye Movements

Objective: We present a physiologically motivated eye movement analysis framework for model-based separation, detection, and classification (MBSDC) of eye movements.By estimating kinematic and neural controller signals for saccades, smooth pursuit, and fixational eye movements in a mechanistic model of the oculomotor system we are able to separate and analyze these eye movements independently.Methods: We extended an established oculomotor model for horizontal eye movements by neural controller signals and by a blink artifact model.To estimate kinematic (position, velocity, acceleration, forces) and neural controller signals from eye position data, we employ Kalman smoothing and sparse input estimation techniques.The estimated signals are used for detecting saccade start and end points, and for classifying the recording into saccades, smooth pursuit, fixations, post-saccadic oscillations, and blinks.Results: On simulated data, the reconstruction error of the velocity profiles is about half the error value obtained by the commonly employed approach of filtering and numerical differentiation.In experiments with smooth pursuit data from human subjects, we observe an accurate signal separation.In addition, in neural recordings from non-human primates, the estimated neural controller signals match the real recordings strikingly well.Significance: The MBSDC framework enables the analysis of multi-type eye movement recordings and provides a physiologically motivated approach to study motor commands and might aid the discovery of new digital biomarkers.Conclusion: The proposed framework provides a model-based approach for a wide variety of eye movement analysis tasks.
Realization of a Hybrid Locally Connected Extreme Learning Machine With DeepID for Face Verification

Most existing state-of-the-art deep learning algorithms discover sophisticated representations in huge datasets using convolutional neural networks (CNNs) that mainly adopt backpropagation (BP) algorithm as the backbone for training the face recognition problems.However, since decades ago, BP has been debated for causing trivial issues such as iterative gradient-descent operation, slow convergence rate, local minima, intensive human intervention, exhaustive computation, time-consuming, and so on.On the other hand, a competitive machine learning algorithm called extreme learning machine (ELM) emerged with extreme fast implementation and simple in theory has overcome the challenges faced by BP.The ELM advocates the convergence of machine learning and biological learning for pervasive learning and intelligence and has been extensively researched in widespread applications.Nonetheless, till date, none of the work of ELM has proved its competency in tackling face verification problem.Hence, in this paper, we are going to probe for the first time the feasibility of ELM-based network in handling the face verification task.We devise and propose a novel and distinguished hybrid local receptive field-based extreme learning machine with DeepID (hereinafter denoted as H-ELM-LRF-DeepID), to discriminate face pairs.The experimental results on the YouTube face database, labeled faces in the wild (LFW), and CelebFaces datasets have shed light upon the feasibility and usefulness of the H-ELM-LRF-DeepID in the face verification task.INDEX TERMS DeepID, extreme
Web Application Resource Requirements Estimation Based on the Workload Latent Features

Most cloud computing platforms offer reactive resource auto-scaling mechanisms for dealing with variable traffic patterns to deliver the desired QoS properties while keeping low provisioning costs. However, a range of scenarios have not been fully addressed by the current auto-scaling solutions, particularly dealing with a rapid increase in workload and the risk of thrashing due to frequent workload variations. A reactive system is vulnerable in such conditions. Realizing the full potential of auto-scaling still remains challenging particularly due to the need of accurately estimating the application resource requirements for time-varying workload patterns. In this work, we propose and evaluate a novel method using only application access logs to estimate more accurately the hardware resource demands and application response time. In particular, we propose novel workload latent features which we compute by applying unsupervised learning on the access logs. We use these latent features to estimate the application hardware resource requirements and response time for various workload patterns. We evaluate the proposed method using multiple benchmark web applications and compare it with current state-of-the-art. Extensive experimental evaluations show an excellent performance of our proposed workload latent features in estimating response time, CPU, memory, and bandwidth utilization.
A Process for Extracting Knowledge Base for Chatbots from Text Corpora

This paper presents a process for extraction of AIML chatbot knowledge bases from text corpora using the AGATA (Automatic Generation of AIML from Text Acquisition) system, developed to facilitate and reduce the time needed in that task. In order to illustrate how the process works, a Pilot Study was carried out, which consisted of converting the content of two textbooks into the AIML language and sending the files to a chatbot knowledge base, being finally assessed by experts in the domain of the content. The results show the potential of helping teachers to easily create their own chatbots according to their course topics.
Task-embedded online eye-tracker calibration for improving robustness to head motion

Remote eye trackers are widely used for screen-based interactions. They are less intrusive than head mounted eye trackers, but are generally quite sensitive to head movement. This leads to the requirement for frequent recalibration, especially in applications requiring accurate eye tracking. We propose here an online calibration method to compensate for head movements if estimates of the gaze targets are available. For example, in dwell-time based gaze typing it is reasonable to assume that for correct selections, the user's gaze target during the dwell-time was at the key center. We use this assumption to derive an eye-position dependent linear transformation matrix for correcting the measured gaze. Our experiments show that the proposed method significantly reduces errors over a large range of head movements.
An Efficient MapReduce-Based Parallel Processing Framework for User-Based Collaborative Filtering

User-based collaborative filtering is one of the most-used methods for the recommender systems. However, it takes time to perform the method because it requires a full scan of the entire data to find the neighboring users of each active user, who have similar rating patterns. It also requires time-consuming computations because of the complexity of the algorithms. Furthermore, the amount of rating data in the recommender systems grows rapidly, as the number of users, items, and their rating activities tend to increase. Thus, a big data framework with parallel processing, such as Hadoop, is needed for the recommender systems. There are already many research studies on the MapReduce-based parallel processing method for collaborative filtering. However, most of the research studies have not considered the sequential-access restriction for executing MapReduce jobs and the minimization of the required full scan on the entire data on the Hadoop Distributed File System (HDFS), because HDFS sequentially access data on the disk. In this paper, we introduce an efficient MapReduce-based parallel processing framework for collaborative filtering method that requires only a one-time parallelized full scan, while adhering to the sequential access patterns on Hadoop data nodes. Our proposed framework contains a novel MapReduce framework, including a partial computation framework for calculating the predictions and finding the recommended items for an active user with such a one-way parallelized scan. Lastly, we have used the MovieLens dataset to show the validity of our proposed method, mainly in terms of the efficiency of the parallelized method.
Superpixel Labeling Priors and MRF for Aerial Video Segmentation

Video segmentation is a task of partitioning pixels that exhibit homogeneous appearance and motion into coherent spatial-temporal groups, which is still challenging for aerial applications. In this paper, a principled combination of superpixel labeling priors and Markov random field (S-MRF) is proposed for aerial video segmentation. The proposed approach has several contributions: 1) we develop a metadata-based global projection model with coordinate transformation to estimate motion information between frames; 2) the superpixel labeling priors from previous frames are incorporated into the segmentation of the current frame, leading to a highly efficient probabilistic label propagation algorithm; and 3) we perform an MRF optimization on the initial segments with propagated labeling priors to improve the temporal coherency. In addition, a new video dataset is collected and will be made publicly available to evaluate the performance of aerial video segmentation algorithms. The experimental results show that the proposed approach outperforms the state-of-the-art video segmentation methods.
Accurate Boundary Detection and Refinement for Continuous Objects in IoT Sensing Networks

This article proposes a two-stage boundary detection and refinement mechanism in Internet of Things sensing networks, where boundary faces are generated by adopting planarization algorithms based on sensory data provided by backbone nodes. Candidate edge nodes, which may be observed more appropriate to serve as inner or outer boundary nodes, are discovered leveraging their sensory data estimated through adopting spatial interpolation methods, and their sensory data are gathered by mobile nodes with certain time constraints. This boundary refinement procedure is conducted in an iteration fashion, until no candidate edge nodes exist in, and thus, the refinement can hardly be further conducted upon original boundary faces. Experimental evaluation demonstrates that this technique can generate a more accurate boundary region for continuous objects than the state of the art.
Component-based development of adaptive user interfaces

Modern User Interfaces (UIs) are increasingly expected to be plastic in the sense that they retain a constant level of usability, even when subjected to context changes at runtime. Adaptive UIs (AUIs) have been promoted as a solution for context variability due to their ability to automatically adapt to the context-of-use at runtime. However, development of AUIs is a complex task as different aspects such as context monitoring and UI adaptation have to be supported. In previous work, model-driven engineering approaches were proposed to support the development of AUIs in a systematic and efficient manner. However, existing model-driven development approaches for AUIs face challenges regarding flexibility, reusability, and compatibility to de facto standard UI frameworks like Angular, which hinder their industry-wide usage and adoption in practice. To address this problem and explore an alternative approach, we propose a component-based development framework for AUIs (CoBAUI). CoBAUI defines a modular framework for supporting the development of AUIs and consists of various components to cover aspects like context monitoring and UI adaptation at widget level. The CoBAUI framework was implemented based on Angular and aims to support the development of AUIs through highly reusable and flexible components. We demonstrate the benefit of our CoBAUI framework based on a case study of an AUI for a library web application.