Reviewerly/exHarmony huggingface dataset:
SWaP-C Based Comparison of Onboard Computers for Unmanned Vehicles

When designing an autonomous vehicle, the size, weight, and power consumption, as well as cost (SWaP-C) of an onboard computer are strictly limited. At the same time, modern algorithms of localization, computer vision, and mapping, which must be run to ensure autonomy, can have high computational complexity. This article proposes a method, which analyzes SWaP-C factor as well as a performance of modern onboard computers to establish restrictions on the space of computers that meet the required limits. To achieve that, the dataset of existing embedded computers is collected, and the mathematical model of onboard computers is suggested. Based on the proposed model, the Pareto optimal set is found. It is shown that the size of the set of computers required to be considered after optimization is on average five times smaller than the initial set.
The Development of Storytelling multimedia Book for Promotion of the Moral and Ethics for Hearing Impaired Students

The intention of the study was to develop a storytelling multimedia book for promotion of the moral and ethics skills for hearing impaired students. The concept of the research was based on the 5 steps of ADDIE MODEL: 1) analysis, 2) design, 3) development, 4) implement, and 5) evaluation. For data collection the use of storytelling multimedia book with 15 hearing impaired students was chosen as method. The data analyses were based on the mean percentage and standard deviation. The results of the study indicated about the storytelling electronic book for promotion of the moral and ethics for hearing impaired students evaluated by experts. The values determined by experts were m = 4.53, s = 0.58 which referred to very good, 1) the design of learning was average (m = 4.3, s = 0.58), 2) the design of multimedia was average (m = 4.6, s = 0.50) and 3) interaction design (m = 4.55)., s = 0.61). After test, the researcher interviewed 15 hearing impaired students, individually, and analyzed the results of word frequencies. The students described the using of storytelling electronic book for promotion of the moral and ethics adjectives such as "interesting," "amazing" and "fun." While the others described feeling happy about the storytelling electronic book: "I am happy to learn from stories", "I had fun when I learned these stories." In terms of content of story, hearing impaired students responded that they understood the content and excited that it was "easy" to understand and the program could interact with user and computer too.
Analysis of the Attitude of Social Network Analysis using R3 Tool

Web based life are impacting people inclinations by forming their demeanours and behaviours. Social media have developed more consideration nowadays. Public and Private assessment about a wide assortment of subjects are communicated and spread persistently by means of various social media. Instagram is one of the web-based life that is picking up notoriety and legitimate marketing. Instagram rules the computerized showcasing space, followed intently by Facebook. Sentiment examination identifies with the issue of mining the feelings from online accessible information and classifying the notion communicated by a specific substance into at most three present categories: positive, negative and neutral. This Paper focus on Instagram presentations, explore the feelings of Instagram life and make positive things a reality. First, a subjective to tests related to human feelings. Individuals have diverse sorts of emotions. we compute just the four kind of feelings like Happy, Sad, Anger, Fear, Surprise, Beauty and Excitement. To arrange posts reflecting Instagrammers feelings through multilevel order calculations is actualized. N Linear Support Vector Machine Learning calculations are utilized. The performance of this calculation is analysed for accuracy, reliability, validation and measurement of F1. Calculations of vector modelling machines are more accurate than the Naïve Bayes algorithm.
Extraction of Taxonomic Relation of Complex Terms by Recurrent Neural Network

In recent years, while the Internet has brought various technological evolutions, a lot of ontology is required to organize and systemize knowledge, and its generation is necessary. Especially, classification of hypernym-hyponym relation which describes taxonomy of ontology has received a lot of attention. As a method to automate the generation, word embedding based method was proposed recently. Although the method enabled high accuracy classification by using semantics, it does not correspond to complex term consisting of multiple words. Based on this background, in this paper, we proposed a new model combined word embedding and Recurrent Neural Network(RNN), evaluated the classification performance with data extracted from WordNet. For the result, it is indicated that the RNN approach is more effective and general for ontology generation.
Component-Based Gender Identification Using Local Binary Patterns

In this paper a component-based gender identification model from facial images has been proposed. The paper enhances the gender identification by using individual facial components (forehead, eyes, nose, cheeks, mouth and chin). Group of frontal facial images are used to validate the proposed model, feature extraction technique Local Binary Patterns (LBP) is implemented, then KNN and SVM classification techniques are applied to accomplish the gender identification model. The results achieved in this research work show an improved accuracy rate when face components (eyes, nose, mouth) are used for gender identification instead of the whole facial image. These results indicate that there are some facial parts which are not necessary for facial image recognition related application like gender identification.
Cluster-Based Join for Geographically Distributed Big RDF Data

Federated RDF systems allow users to retrieve data from multiple independent sources without needing to have all the data in the same triple store. The performance of these systems can be poor for large and geographically distributed RDF data where network transfer costs are high. This paper introduces CBTP, a novel join algorithm that takes advantage of network topology to decrease the cost of processing SPARQL queries in a geographically distributed environment. Federation members are grouped in clusters, based on the network communication cost between the members, and the bulk of the join processing is pushed to the clusters. We use an overlap list to efficiently compute join results from triples in different clusters. We implement our algorithms in OpenRDF Sesame federated framework and use Apache Rya triple store instances as federation members. Experimental evaluation results show the advantages of our approach over existing techniques.
Ontology Based Personalization of Mobile Interfaces for People with Special Needs

The paper is devoted to a problem of interface personification for people with special needs on the base of information about their behavior during interaction with mobile applications. This work evolves our previous researches on the development of adaptive user interfaces. Much attention in this paper was given to the investigation of existing approaches to collecting and analyzing the information about user behavior and interaction context data as well as interface adaptation recommendations. The improved interface adaptation mechanism was developed and described based on the ontological representation of the interface patterns and knowledge about users and their interaction with a mobile application. The set of adaptation rules was developed and implemented in the ontology knowledge base. In the paper, we described the improved ontology model and some examples of ontological representation of interface patterns.
Foreground extraction via dual-side cameras on a mobile device using long short-term trajectory analysis

This paper presents a foreground extraction method for live-streaming videos using dual-side cameras on mobile devices. Compared to conventional methods, which estimate both foreground and background models from the front camera, the proposed method uses the rear camera to infer the reference background model. To this end, the short-term trajectory analysis is first performed to cluster point trajectories of the front camera, and then the long-term trajectory analysis is performed to compare the paths of the clustered trajectories with the reference path obtained from the rear camera. In particular, clusters having high correlation are classified as background using the Gaussian mixture model. Additionally, a pixel-wise segmentation map is obtained via graph-based segmentation. Experimental results show that the proposed method is robust under a variety of camera motion, outperforming state-of-the-art methods. Code and dataset can be found at https://github.com/YCL92/dualCamSeg.
Distributing an SQL Query over a Cluster of Containers

Emergent software container technology is now available on any cloud and opens up new opportunities to execute and scale data intensive applications wherever data is located. However, many traditional relational databases hosted on clouds have not scaled well. In this paper, a framework and deployment methodology to containerize relational SQL queries is presented, so that, a single SQL query can be scaled and executed by a network of cooperating containers, achieving intra-operator parallelism and other significant performance gains. Results of container prototype experiments are reported and compared to a real-world RDBMS baseline. Preliminary result on a research cloud shows up to 3-orders of magnitude performance gain for some queries when compared to running the same query on a single VM hosted RDBMS baseline.
Decomposed slice sampling for factorized distributions

Slice sampling provides an automatical adjustment to match the characteristics of the distribution. Although this method has made great success in many situations, it becomes limited when the distribution is complex. Inspired by Higdon [1], in this paper, we present a decomposed sampling framework based on slice sampling called decomposed slice sampling (DSS). We suppose that the target distribution can be divided into two multipliers so that information in each term can be used, respectively. The first multiplier is used in the first step of DSS to obtain horizontal slices and the last term is used in the second step. Simulations on four simple distributions indicate the effectiveness of our method. Compared with slice sampling and Hamiltonian Monte Carlo on Gaussian distributions in different dimensions and ten real-world datasets, the proposed method achieves better performance.
Cost-Efficient Stream Processing on the Cloud

Under dynamic workload, cost-efficient stream processing on the Cloud, where the cost for Cloud resources is minimized and the performance is optimized, is non-trivial to achieve. To tackle this, we make use of queueing theory to model the performance of stream processing components. We propose a stable performance and cost trade-off scaling algorithm for large-scale stream processing on the Cloud. Our approach takes into consideration typical characteristics of Cloud environments such as the machine size, dynamic cost, high machine start-up costs and large migration time of Cloud resources. We present a strategy based on a multi-step configuration to enable dynamic stream processing on the Cloud while cost-efficiently achieving user-level Quality of Service (QoS). Our proposed solution addresses the issues of resource provisioning with respect to both the performance objectives and dynamic scheduling of Cloud environments. We present an exemplar implementation, based on a Heron stream processing application that was deployed automatically and subsequently monitored to ensure its QoS.
Multiple Motion Fields for Multiple Types of Agents

Complex surveillance scenarios comprise different types of agents (e.g., bikers, cars, and pedestrians) that must be efficiently characterized in order to facilitate tasks such as tracking or abnormality detection. This paper proposes an unsupervised hierarchical multiple motion fields model to represent different types of agents, which relies in the combination of hierarchical Markov model and velocity fields. Model parameters are estimated using the expectation-maximization algorithm. The proposed framework was applied to synthetic and real datasets (Stanford Drone Dataset), showing the ability to characterize and classify different agents in an unsupervised way.
Synthetic Data Generation Approach for Face Recognition System

User identification within smart environments (SE) is intended to organize functioning of all services efficiently. One of the most popular ways to recognize a user is by his or her face. It is necessary to have a large dataset as it is possible to train an accurate classifier, especially if deep neural networks are being used. It is expensive to organize representative dataset manually to take a photograph of every person from every possible angle with every possible light condition. This is why the generation of synthetic data for training a classifier, using minimum real data is so urgent. In this paper, we propose an approach for the generation synthetic user’s face images with different head rotation and lighting conditions.
Weight empowerment method in information fusion for radar‐seeker performance evaluation

With the continuous development of radar terminal guidance technology, the evaluation of the performance of a radar seeker has become a very necessary and important system subject. Rationally allocating the index weight has always been an important spot in the research of performance evaluation. For this problem, a new method based on importance level to allocate the index weights is proposed, which can both reflect the subjective opinion of an expert and the objective facts. Second, rather than evaluate radar seeker performance in simulation test and outfield experiment, respectively, information fusion is applied to evaluate the radar performance instead of serial-phased scheme which takes advantage of both infield simulated and outfield measured data. Furthermore, a model was proposed to calculate the weights of each information element in the information-fusion stage. Considering that the performance evaluation of a radar seeker involves many experimental stages, information fusion which can make full use of the multi-source test information can help to obtain more accurate and reliable evaluation conclusion. Both the theoretical analysis and experimental results prove the effectiveness and efficiency of the proposed method.
A Software Prototype for Multidimensional Design of Data Warehouses Using Ontologies

One of the key success points of a data warehousing project is the design of the multidimensional schema. Many researches shows that the use of ontologies in information system design is becoming more and more promising. In this paper, we propose a method for multidimensional design of data warehouses, from an operational data source, using ontologies. Furthermore, we introduce the concept of multidimensional ontology as a tool for the specification of multidimensional knowledge. In addition, we present an ontology-based method for data modeling schema that eventually covers different phases of the data warehouse life cycle, and takes into account the users by considering their personalized needs as well as their knowledge of the domain.
Zero-Shot Learning Using Stacked Autoencoder with Manifold Regularizations

Zero-shot learning (ZSL), which focuses on transferring the knowledge from the seen classes to unseen ones, has attracted more and more attention in the computer vision community. Exploring the relationships among the spaces of visual representation, semantic description and label information is a key to the success of ZSL. In this paper, we propose a novel approach by using a two-layer Stacked AutoEncoder (StAE) with manifold regularizations to construct the tight relations of different spaces, where the first-layer encoder aims to project a visual feature vector into the semantic space, and the second-layer encoder connects the semantic description of a sample with its label directly. Meanwhile, the decoders seek to reconstruct the visual representation from label information and semantic description successively. Besides, two manifold regularizers are integrated in the stacked autoencoder, which captures the manifold structures residing in the different spaces effectively. Compared with the previous related works, the proposed approach is a more general framework and has stronger transfer ability from seen classes to unseen classes. Extensive experiments on the benchmark datasets clearly demonstrate that our StAE performs significantly better than the state-of-the-arts.
Negacyclic Codes of Length $2^mp^n$ Over Finite Fields

Let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{q}$ </tex-math></inline-formula> be a finite field of odd order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> . Let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> be a positive integer such that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X^{2^{m}}+1$ </tex-math></inline-formula> factors completely into degree-one factors in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{q^{2}}[X]$ </tex-math></inline-formula> . The polynomial generators of all negacyclic codes of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{m}p^{n}$ </tex-math></inline-formula> over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{q}$ </tex-math></inline-formula> are obtained, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> is an odd prime coprime to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> .
FlashGhost: Data Sanitization with Privacy Protection Based on Frequent Colliding Hash Table

Today's extensive use of Internet creates huge volumes of data by users in both client and server sides. Normally users don't want to store all the data in local as well as keep archive in the server. For some unwanted data, such as trash, cache and private data, needs to be deleted periodically. Explicit deletion could be applied to the local data, while it is a troublesome job. But there is no transparency to users on the personal data stored in the server. Since we have no knowledge of whether they're cached, copied and archived by the third parties, or sold by the service provider. Our research seeks to provide an automatic data sanitization system to make data could be self-destructing. Specifically, we give data a life cycle, which would be erased automatically when at the end of its life, and the destroyed data cannot be recovered by any effort. In this paper, we present FlashGhost, which is a system that meets this challenge through a novel integration of cryptography techniques with the frequent colliding hash table. In this system, data will be unreadable and rendered unrecoverable by overwriting multiple times after its validity period has expired. Besides, the system reliability is enhanced by threshold cryptography. We also present a mathematical model and verify it by a number of experiments, which demonstrate theoretically and experimentally our system is practical to use and meet the data auto-sanitization goal described above.
Nonparametric Learning Via Successive Subspace Modeling (SSM)

A novel nonparametric machine learning methodology, called successive subspace modeling (SSM), is proposed in this work. Without loss of generality, we use image classification as an illustrative example. The SSM procedure consists of two stages: 1) feature learning and 2) decision learning. For feature learning, we partition input images into overlapping patches of different sizes recursively. While the input images define a vector space, patches of smaller sizes form a sequence of growing subspaces. From the smallest to the largest subspaces, we build a model for each subspace in a successive manner through the Saab transform. At the end, we obtain a lower-dimensional feature vector space that contains significant spatial-spectral information of input images. For decision learning, we summarize the distribution of feature vectors with two techniques; namely, feature space partitioning and local manifold learning. Then, for every test sample, an ensemble decision is made based on decisions at each of its near clusters. The superior performance of SSM is demonstrated on the MNIST dataset.
Canonical mixed-polarity multi-target Toffoli circuits: Shift and removal

In this paper, we study reversible circuits as cascades of multi-target Toffoli gates. This type of gates allows to shift parts of a gate to the preceding gate within a circuit provided that a certain independence condition holds. It turns out that shifts decrease the so-called waiting degree such that shifting as long as possible always terminates and yields shift-reduced circuits. As the main result, we show that shift-reduced circuits are unique canonical representatives of their shift equivalence classes. Canonical circuits are optimal with respect to maximal and as-early-as-possible parallelism of targets within gates. Further, we discuss how successive equal subgates can be removed in order to reduce the waiting degree even more. Interestingly after applying shifts and removals as long as possible again a unique normal form is obtained.
A Framework for Research Publication Recommendation System

Information overload is one of the main problem of nowadays information retrieval systems. To obtain relevant information or items, many users use recommendation systems which are commonly available: for products in Internet stores, musics, books, etc. Also in the field of research papers it is hard to find relevant items. There exists many scientific search engines that retrieve huge databases to find best papers but it would be comfortable to have an ability to find the best journal or conference where to publish current paper. Every researcher receive many “calls for papers” but many times the propositions are rather random and a little correlated with our research. In this paper we explore possibilities of collaborative filtering and content-based approaches to Publication Recommender System. We have presented a simple case study for a selected group of users.
Color Image Denoising Using Quaternion Adaptive Non-Local Coupled Means

Because quaternion representation is able to preserve the relationship among RGB channels of a color image, we take advantage of this characteristic and propose a new color image denoising method, named the Quaternion Adaptive Non-local Coupled Means (QANLCM). QANLCM first builds a four-variable optimization model by decomposing the original noisy image into a luminance component and a chromaticity component, and then alternatively updates the four variables in each denoising iteration. Simulations and comparisons demonstrate that QANLCM shows a superiority to other non-local means methods in removing Gaussian noise from color images.