Reviewerly/exHarmony huggingface dataset:
A System for Noncontact Estimation of Cognitive Load Using Saccadic Parameters Based on a Serio-Parallel Computing Framework

This paper proposes a system which uses a three-stage serio-parallel video-oculographic framework for computing the saccadic eye parameters to indicate the amount of cognitive loading. The three stages are viz., face and eye detection, iris and eye corner localization, and finally saccadic parameter computation. Since saccades are fast movements of the eyeballs, accurate estimation of these parameters requires high frame rates of acquisition and processing. Our proposed framework meets such deadlines by accelerating the process using graphics processing units (GPUs). The first stage comprises the face and eye detection using the respective Haar classifiers followed by tracking of a region of interest using a minimum output sum of squared error filter. In the second stage, the filter parameters are transferred to the GPU, where our proposed parallel scheme is implemented. In the detected eye region, the iris candidates are ranked using a sum of dot products of normalized displacement vectors with gradient vectors. We also localize the eye corners as the reference points. The saccadic velocity and duration are obtained using this eye position signal in the third stage. Finally, the amount of cognitive loading is determined based on these parameters.
Frame Logic‐based specification and discovery of semantic web services with application to medical appointments

Abstract Matching web services and client requirements in the form of goals is a significant challenge in the discovery of semantic web services. The most common but unsatisfactory approach to matching is set‐based, where both the client and web services declare what objects they require and what objects they can provide. Matching then becomes the simple task of comparing sets of objects. This approach is inadequate because it says nothing about the functionality required by the client or the functionality provided by the web service. As an alternative, we use the Frame Logic as implemented in Flora‐2 to specify web service capabilities and client requirements, including their preconditions, postconditions, and ontologies, implement a logic‐based discovery agent using Flora‐2, demonstrate its usefulness in a medical appointment making scenario, and show its efficiency both theoretically and by benchmarking. The result is an expressive yet concise representation scheme for semantic web services, and a practical, efficient, powerful, and fully implemented matching engine based purely on logical inference for web service discovery, with direct applicability to Web Service Modeling Ontology and Web Service Modeling Language, because both are based on Frame Logic.
Design principles and architecture of the system “Processing and analysis of images and video streams”

In this paper, we present design principles and architecture of the system "Processing and analysis of images and video streams". It contains test examples of images and techniques for their processing based on some classical and modern grid methods. Note that our DSS is open in the sense that it contains modules for creating user databases and the reports base "Algorithms performance quality", as well as for adding a new grid method or a new application module in the DSS. The logic of the actions of these modules is presented in the diagrams of UML. Application modules demonstrate the use of methods for processing and analyzing images and video streams by industry, for example, analyzing the surface quality of a porous material, monitoring the level of liquid in a container, etc. This approach ensures the close connection of the DSS with modern scientific achievements. This is the realization of principles of learning, versatility, and scientific approach.
Deterministic Partially Self-avoiding Walks on Networks for Natural Shapes Classification

Shape is an important characteristic used by different classification tasks in computer vision. In particular, shape is useful in many biological problems (e.g. plant species recognition and fish otolith classification), which are challenging due to the diversity found in nature. This paper proposes a novel method for shape analysis and classification based on deterministic partially self-avoiding walks (DPSWs) on networks. First, a shape contour is modeled as a network by mapping each contour pixel as a vertex. Then, deterministic partially self-avoiding walks are performed on the network and a robust shape signature is obtained using statistics of the trajectories of the DPSWs. We evaluate this feature vector in a classification experiment using two different natural shape databases: USPLeaves and Otolith. The experimental results demonstrate a high classification accuracy of the method when compared to the other methods. This suggests that our method is a promising option for the classification task in biological problems.
Parallel Weighted Random Sampling

Data structures for efficient sampling from a set of weighted items are an important building block of many applications. However, few parallel solutions are known. We close many of these gaps. We give efficient, fast, and practicable parallel and distributed algorithms for building data structures that support sampling single items (alias tables, compressed data structures). This also yields a simplified and more space-efficient sequential algorithm for alias table construction. Our approaches to sampling k out of n items with/without replacement and to subset (Poisson) sampling are output-sensitive , i.e., the sampling algorithms use work linear in the number of different samples. This is also interesting in the sequential case. Weighted random permutation can be done by sorting appropriate random deviates. We show that this is possible with linear work. Finally, we give a communication-efficient, highly scalable approach to (weighted and unweighted) reservoir sampling. This algorithm is based on a fully distributed model of streaming algorithms that might be of independent interest. Experiments for alias tables and sampling with replacement show near linear speedups using up to 158 threads of shared-memory machines. An experimental evaluation of distributed weighted reservoir sampling on up to 5,120 cores also shows good speedups.
Safety First? Users’ Perception of Wearable Sensor Networks for Aging

Population aging is considered to be one of the most significant social transformations of the twenty-first century. In this paper, we evaluate the acceptability and usability of a system designed to silently monitor the activity and condition of older adults, with a particular focus on the elderly affected by cognitive decline. The system is composed of sensors that can be hidden in various wearable objects to collect data about a user and his/her surrounding environment and transmit them to a server for further elaboration. The aim is to improve safety and independence of older adults. We conducted a survey involving elderly people, their relatives and health practitioners to better assess their needs and provide them with a technology that is as little intrusive and visible as possible. The results of our survey are promising as the majority of the respondents expressed their willingness to use the proposed technology in their everyday lives.
High-speed projector and its applications

We have developed a high-speed and low-latency projector DynaFlash that can display 8bit black and white images at up to 1,000fps with 3ms delay. Recently we have also developed a high-speed projector DynaFlash v2 that can display 8bit color images at 947fps. These projectors use DMDs and time domain modulation of high-brightness LEDs. In addition, a newly designed communication interface module is installed in a computer for transferring images at high speed. This reduced the delay from image generation to projection to 3ms at best. On the other hand, we developed 1,000fps highspeed image processing that can recognize three dimensional shape and center position of moving objects every 1ms. A combination of the high-speed projector and a high-speed visual tracking system based on the high-speed vision realizes a dynamic projection mapping (DPM) system that can make a realistic projection mapping onto a moving object by using usual 60fps projector. As the high-speed vision can recognize three dimensional shapes of flexible objects every 1ms, so we can calculate appropriate projection images from photos or movies based on the three dimensional shape and position of objects. The high-speed projector displays them onto deformable objects such as T-shirts, even if the shape of objects changes quickly. In this paper, basic architectures of those systems and devices will be explained. In addition, application systems of highspeed projectors including DPM will be also shown.
A Lightweight Identity-Based Cloud Storage Auditing Supporting Proxy Update and Workload-Based Payment

Cloud storage auditing allows the users to store their data to the cloud with a guarantee that the data integrity can be efficiently checked. In order to release the user from the burden of generating data signatures, the proxy with a valid warrant is introduced to help the user process data in lightweight cloud storage auditing schemes. However, the proxy might be revoked or the proxy’s warrant might expire. These problems are common and essential in real-world applications, but they are not considered and solved in existing lightweight cloud storage auditing schemes. In this paper, we propose a lightweight identity-based cloud storage auditing scheme supporting proxy update, which not only reduces the user’s computation overhead but also makes the revoked proxy or the expired proxy unable to process data on behalf of the user any more. The signatures generated by the revoked proxy or the expired proxy can still be used to verify data integrity. Furthermore, our scheme also supports workload-based payment for the proxy. The security proof and the performance analysis indicate that our scheme is secure and efficient.
Identification of automata Markov models using a modified “forward-backward” algorithm

In 1989, Rabiner L.R proposed the solution of the problem of identifying hidden Markov models by induction using the modified "forward-backward" algorithm. In this paper, we propose the application of a modification of this method for solving the recognition of automaton Markov models following sequences of finite simple homogeneous Markov chains generated on the basis of stochastic ergodic matrices, which in turn determine the class of the automata Markov model.
Challenges and Future Directions for Data Management in the Geosciences

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).CORRESPONDING AUTHOR: Douglas C. Schuster, schuster@ucar.edu
3D image encryption based on computer-generated hologram in the fractional Fourier domain

In this paper, we propose a robust 3D image encryption scheme based on computer-generated hologram (CGH) in fractional Fourier domain. The layer-based Fresnel transform is utilized to generate one phase-only CGH, which is then decomposed into two phase-only masks (POMs) by pixel superimposed method. Encryption was realized by using the created POMs in two cascaded fractional Fourier transform domains while two decryption keys are produced in the encryption process. The cryptosystem is asymmetric and high resistance against to the various potential attacks, including chosen-plaintext attack (CPA). The proposal is supported with computer simulation results. Simulation results and security analysis verify the feasibility and effectiveness of the proposed encryption scheme.
Use SMO SVM, LDA for Poet Identification in Arabic Poetry

This work study a poet identification in Arabic poetry using classification methods, with features like poetry features, Sentence length, Characters, word length, first word in the poetry sentence and specific words are used as input data for text classification algorithms: Sequential Minimal Optimization (SMO), Support Vector Machine (SVM), and Linear discriminant analysis (LDA). The data set of experiment divided into two parts: a training dataset with known poets and test dataset with unknown poets. In our experiment, a set of 114 poets from entirely different eras are used. The researcher shows exciting results with a classification accuracy of 98.2456%.
Improving Efficiency of Remote Construction by using Adaptive Video Streaming

Although areas devastated by natural disasters must be promptly reconstructed, reconstruction works involving construction machinery are dangerous and bring a significant risk of a second disaster. A remote construction system is thus often used, which enables an operator to conduct reconstruction work from a remote and safe place while viewing on-site videos. However, the remote construction has a major problem: it has much lower work efficiency than manned construction because videos are delayed and impaired in a wireless network environment. In this paper, we present a remote construction system using our proposed adaptive video streaming method that dynamically adjusts the video bitrate on the basis of the predicted communication throughput variation. The experiments show that the proposed remote construction system using adaptive video streaming reduces operation time by 25.5%, which corresponds to a 34.3% improvement in work efficiency.
A conditional access system with revocation for mobile pay-TV systems revisited

In pay-TV, conditional access systems (CAS) are used by a rights issuer to guarantee that only authorized subscribers gain access to TV channels. A CAS scheme that applies attribute-based access control through attribute-based encryption (ABE) with revocation was proposed by Yeh and Huang (2013) [1]. Yeh and Huang extend an existing ABE scheme with a revocation mechanism. We show that the CAS by Yeh and Huang has security and efficiency problems. Particularly, we show that the revocation mechanism proposed by Yeh and Huang is vulnerable to collusion attacks.
Multimodal Object Recognition Using Deep Learning Representations Extracted from Images and Smartphone Sensors

In this work, we present a multimodal approach to perform object recognition from photographs taken using smartphones. The proposed method extracts neural codes from the input image using a Convolutional Neural Network (CNN), and combines them with a series of metadata gathered from the smartphone sensors when the picture was taken. These metadata complement the visual contents and they can provide additional information in order to determine the target class. We add feature selection and metadata pre-processing, by encoding textual features, such as the kind of place where a picture was taken, using Doc2Vec in order to maintain the semantics. The deep representations extracted from images and metadata are combined with early fusion to classify samples using different machine learning methods (k-Nearest Neighbors, Random Forests and Support Vector Machines). Results show that metadata preprocessing is beneficial, SVM outperforms kNN when using neural codes on the visual information, and the combination of neural codes and metadata only improves the results slightly when the images are classified into very general categories.
Coflow Scheduling in the Multi-Resource Environment

In data centers, a lot of cluster computing applications follow the coflow working pattern. That is, a collection of flows between two groups of machines is semantically related. On the other hand, network function virtualization sufficiently improves the performance of data center networks. It however complicates the network environment by introducing many multi-function middleboxes each with multiple resources. Coflows encounter extremely different processing delays under diverse network functions. Prior coflow scheduling schemes are insufficient to guarantee the coflow completion time in the multi-resource environment. In this paper, we propose, model, and analyze the coflow scheduling problem in the multi-resource environment. We present a dedicated method, data rate guarantee for coflow (DRGC), to guarantee the data rate requirements of coflows in this situation. DRGC prioritizes the coflow scheduling sequence, assigns precise data rates for coflows, and deploys a packet scheduling algorithm at middleboxes to guarantee their transmissions. In our experiments, DRGC efficiently guarantees the completion times of coflows and supports 15% more workload, compared with other scheduling schemes.
Motion Planning and Control of Social Mobile Robot – Part 1. Robot Hardware Architecture and Description of Navigation System

This two-part paper is concerned with the problem of motion planning and control of PIAP IRYS social robot mobile platform, which is the adapted NOMAD 200 robot. In the first part of the work the developed hardware and software architectures of the research object, as well as its kinematic structure are described. The robot consists of two independent components: (1) a head with a trunk and (2) a mobile platform. As the mobile platform the modernized version of NOMAD 200 robot with synchronously driven and steered wheels was used. The mobile platform was equipped among others with modern low-level miControl motor controllers and a high-level controller working under GNU/Linux operating system with Robot Operating System framework. Kinematics of the three-wheeled robot is discussed and software architecture including ROS-based navigation system is described.
On relationship formation in heterogeneous information networks: An inferring method based on multilabel learning

This paper studies how relationships form in heterogeneous information networks (HINs). The objective is not only to predict relationships in a given HIN more accurately but also to discover the interdependency between different type of relationships. A new relationship prediction method MULRP based on multilabel learning (MLL in brief) is proposed. In MULRP, the types of relationship between two nodes are represented by the meta‐paths between nodes and each type of relationship is given a label. Under the framework of MLL, any potential relationships including the target relationship can be predicted. Moreover, the method can output the reasonable dependency scores between relationships. Thus, more viable paths will be provided to facilitate the formation of new relationships. The proposed method is evaluated on two real datasets: The DBLP Computer Science Bibliography(abbr. DBLP) network and Twitter network. The experimental results show that by using heterogeneous information in a supervised MLL setting, MULRP achieves better performance in comparison to several baseline binary classification methods and a state‐of‐art relationship prediction method.
A 2-AFC study to validate artificially inserted microcalcification clusters in digital mammography

The validation of many studies in the field of medical imaging relies on the measurement of sensitivity and specificity of a given task. In breast cancer screening, it is common to validate studies by assessing the impact to the sensitivity and specificity of cancer detection through experiments such as N-alternative forced-choice (N-AFC) or receiver operating characteristics (ROC). In general, these experiments require large datasets of clinical mammograms containing a considerable number of cases with true positive findings. Nonetheless, in clinical practice only a small fraction of the patients (&lt;1%) are actually diagnosed with breast cancer. One common approach to solve this data constraint is to insert lesions to images taken from healthy patients, thus creating a hybrid dataset with real and artificial data. In this work we investigate a simple method to perform the segmentation of microcalcification clusters from clinical cases, followed by the artificial insertion of the clusters into normal (true negative) mammograms. A 2-AFC human observers study was performed, where observers were asked to choose between artificially inserted and real clusters of microcalcifications. Artificially inserted clusters were selected 47.3% of the time, indicating that the readers were not able to distinguish between artificially inserted and real clusters (p = 0.65). A dataset containing 100 BI-RADS 1 clinical mammograms with artificially inserted clusters was visually evaluated by an experienced radiologist, who was asked to comment on the appearance and positioning of the clusters. The reader considered the appearance and location realistic in 98% of the images. Two cases had problems related to patient motion and breast segmentation. The codes and segmented lesions are available for download at: https://github.com/LAVI-USP/MCInsertionPackage.
Airborne visual tracking and reidentification system

Visual tracking and reidentification has gathered a lot of attention in real-world surveillance applications. We explore on-board visual tracking and reidentification of a person from a moving camera, such as a drone. We develop an android application which can communicate with the drone and transmit roll, pitch, and vertical upthrust instructions. We use a kernelized correlation filter tracker for tracking the person, and for the reidentification task we apply a deep three-dimensional ConvNet. The experiments demonstrate a robust performance.
Contextual Deep Search using Long Short Term Memory Recurrent Neural Network

The internet is teeming with an ocean worth of information and combing through all that in order to find what one wants can become a daunting task if one does not possess the right tools and techniques. This paper explores one such technique, exploiting the rapidly responsive Long-Short Term Memory Recurrent Neural Networks (LSTM-RNNs) by harnessing the machine learning capabilities of neural networks and eventually, provides contextual search results at the finger tips of the user of the system.
Evaluating Distributed Computing Infrastructures: An Empirical Study Comparing Hadoop Deployments on Cloud and Local Systems

The popularity of distributed computing platforms (e.g., Hadoop) is largely due to their ability to address scalability issues that arise from data storage and processing limitations of standard computing systems. However, the decision to dedicate organizational resources and capital for such systems needs a careful consideration of several factors including evaluation of cloud-based distributed computing options. We propose a framework of metrics which we used to conduct an in-depth performance and cost benefit analysis of two standard Hadoop infrastructural choices, i.e., a Platform as a Service ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PaaS</i> ) on-demand cloud setup and a local organizational setup. We evaluated the framework by means of an exploratory data analysis use-case for a large-scale graph processing research problem. Our analysis considered highly granular aspects of distributed computing performance and studied how utilization rates and infrastructure amortization times affect break-even times. We identified that virtual memory management adversely affects the performance of a cloud cluster during the reduce phase with the magnitude of degradation dependent on the type of MapReduce operation. Our study is intended not only as an evaluation of infrastructural choices but also a development of a metric framework that can serve as a baseline for researchers examining distributed infrastructures.