Reviewerly/exHarmony huggingface dataset:
Fog-Cloud Collaboration for Real-Time Streaming Applications

Real-time streaming applications (RTSAs) generate huge volumes of temporally ordered, infinite, continuous, high speed data streams demanding both real-time and long-term data analytics. Fog computing is a reliable solution for processing and analyzing real-time streaming data as it offers low latency, location-aware, geographically distributed service at fog node and provides long-term services at the cloud data center (DC). This chapter addresses the challenge of coordinating the fog nodes and cloud for efficient processing of real-time streaming data in motion and at rest. The fog-cloud collaboration framework proposed in this chapter employs data stream management system (DSMS) schema at the fog node for real-time stream data processing and response generation. The data representation in micro-clusters at fog node and macro-clusters at DC facilitates accurate data analytics. The coordination between fog node and DC is through local ontology and global ontology respectively.
Hybrid Cryptographic Based Approach for Privacy Preservation in Location-Based Services

Location-based services (LBSs) are one of the dominant technology of current era in the fields of mobile, information communication and networking. User requires many important information based on their location to perform their task like location-based navigation, location-based information, and many more. The user has to give their important information like user identity and location information to the provider that are personalized. Location privacy and communication is the major problem in LBSs. LBSs are categorized as TTP based and TTP free schema. TTP free schema is one of the best schemas which uses cryptographic technique and peer-to-peer communication model. In peer-to-peer model, various authors already proposed various approaches to provide location privacy. But still many open challenges needed to be solved like to improve scalability and reduce communication cost and computational cost. In this paper, we propose an approach that provides location privacy to the LBS users.
A two-party quantum private comparison of equality protocol with better performance in resisting outside attack

Private Comparison (PC) is an extension of Yao's millionaire problem: two millionaires want to know if their wealth is equal, but they don't want to reveal their specific wealth. In classical cryptography, this problem has been well studied. However, the security issues of these protocols are based on computational complexity assumptions, which means they cannot resist powerful quantum computing. In this paper, a quantum private comparison of equality with better performance in resisting outside attack is proposed based on two parties. Compared with the previous protocol, our protocol greatly reduce the total probability of obtaining the wrong result. And it also can compare successfully even if the two hash value string are short and close. Meanwhile, our protocol can be implemented easily without using entanglement, joint measurements and quantum memory and so on. Finally, we show that our protocol can be secure against the attacks from both the outside eavesdroppers and the inside participants.
The Relationship Between Nigerian University Students' Body Literacy Level and Their Awareness of Embodied Learning

Although there is growing global interest in health awareness, and emergence of scales to measure health literacy, there is less effort in empirical research in ascertaining the level of body literacy of different groups of individuals in determining how much information and understanding individuals have about their bodies. The academia may be successful in enhancing cognitive and psychomotor skills of students, but how have these enhanced students' knowledge to issues concerning their bodies and health such as blood pressure, blood sugar, genotype, body mass index, feeding habit, etc., and the decisions/actions they make/take regarding these, and the implications of these to being embodied learners? This study sampled 120 participants to investigate the body literacy level of postgraduate and undergraduate students of Obafemi Awolowo University, Ile-Ife, Nigeria, their level of awareness to risk, and their challenges of being bodily literate through a self-developed questionnaire which translated into a 21-item body literacy measurement scale.
Scaling Cryptographic Techniques by Exploiting Data Sensitivity at a Public Cloud

Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. This poster continues along the emerging trend in secure data processing that recognizes that the entire dataset may not be sensitive, and hence, non-sensitivity of data can be exploited to overcome some of the limitations of existing encryption-based approaches. In particular, this poster outlines a new secure keyword search approach, called query keyword binning (QB) that allows non-sensitive parts of the data to be outsourced in clear-text while guaranteeing that no information is leaked by joint processing of non-sensitive data (in clear-text) and sensitive data (in encrypted form). QB improves the performance of and strengthens the security of the underlying cryptographic technique by preventing size, frequency-count, and workload-skew attacks.
Communication Using Interperspectival Contents

Abstract The problem we encounter with any semantic theory that expresses perspectival content is that when we engage in communication we of necessity communicate with people in other perspectival positions. So, for example, we often speak with people in different physical locations. Sometimes, we communicate across time, e.g. by leaving messages, and we often report on thoughts and comments made at an earlier time. Unless we are talking to ourselves, we also communicate with other persons, who are, obviously, in different perspectival positions. This chapter explores how people routinely communicate across such perspectival positions. The same interperspectival content is expressed in different ways in different perspectival contents. A theory of cognitive dynamics is developed which explains how these modes of expression are coordinated.
Data Mining and Nonlinear Non-stationary Processes Forecasting by Using Linguistic Modeling Method

The report focuses on the actual task of development of mathematical tool for data mining and nonlinear non-stationary processes forecasting. The proposed mathematical tool can use in solving the problems of data analysis of various nature for nonlinear non-stationary processes forecasting. The results of its application for prediction of such processes are present.
Team Exploration of Environments Using Stochastic Local Search

We investigate the use of Stochastic Local Search (SLS) technique to explore environments where agents? knowledge and the time to explore such environments are limited. We extend a work that uses evolutionary algorithms to evolve teams in simulated environments. Our work proposes a formalization of the concept of state and neighborhood for SLS and provides evaluation of agents? teams using number of interesting cells. Further, we modify the environments to include goals that are randomly distributed among interesting cells. Agents in this case are then required to search for goals. Experiments using teams of different sizes show the effectiveness of our technique. Teams were able to complete exploration of more than 70% of the environments, while in the best cases, they were able to complete explorations of more than 80% of the environments within limited time steps. These results compare with those of the previous work. It is interesting to note that all teams of agents were able to find on average all the goals in the three environments when the size of the grid is 12. This is a 100% achievement by the agents? teams. However, performance can be seen to degrade as the environments? sizes become larger.
Procedural Generation for Tabletop Games: User Driven Approaches with Restrictions on Computational Resources

Procedural Content Generation has a focus on the development of digital games, this leaves out a number of interesting domains for a generation of wargames and other board game types. These games are based on limited computational resources, using dice for random numbers and creations via tables. This paper presents a historical look at non-digital methods for PCG application and in particular a taxonomy of methods and locations of their use.
Application research of electronic forensics technology in internet pornography cases

Studying cybercrime investigation and evidence collection has important practical significance for improving the detection rate of network cases and curbing cybercrime. The article expounds the objectives, basic processes and key technologies of electronic forensics. Through a real case, it analyzes the principles and conditions of remote access to the records in the database, and proposes a method for remotely obtaining data to verify the electronic forensics technology in the case of "cyber pornography". The actual forensics process. Finally, the countermeasures and suggestions for the investigation and evidence collection of cybercrime cases are put forward.
Example of nonlinear dynamical parameters for machine learning-based speech pathology classifier

In light of muscle coordinative structures might be defined as time-invariant dynamical systems that underlie an action's form, we find out if the ability of dynamical system to grade the levels of complexity according to external conditions is a feature of speech without sound disorders. Results revealed that such metrics of speech signal as correlation dimension and sample entropy as indicators of complexity vary widely under environmental conditions (acoustic fatigue) for participants without speech sound disorders, unlike for participants, who admitted the ones (sibilant /s/ considered). Supposedly, mentioned parameters may be of interest to machine learning-based speech pathology classifiers.
Geohazards, Extreme Weather Events, and Climate Conditions—The Development of FHWA Guidance

Our nation's transportation system is routinely impacted by geohazards and climate conditions. Recent examples include wildfires followed by rainfall and associated mudslides in California as well as intense rainfall with associated flooding leading to roadway washouts in Texas, Virginia, and Maryland. Guidance is being developed to assist state departments of transportation (DOTs) and federal land management agencies (FLMAs) in developing or improving existing transportation geohazards programs. This includes considerations for climate conditions and extreme weather events. To respond to the threat of geohazards, many state transportation agencies have developed geohazards management programs. A geohazards program helps agencies manage cost-effective methods for characterizing and reducing risk.
Accelerating Spectral Graph Analysis Through Wavefronts of Linear Algebra Operations

The wavefront pattern captures the unfolding of a parallel computation in which data elements are laid out as a logical multidimensional grid and the dependency graph favours a diagonal sweep across the grid. In the emerging area of spectral graph analysis, the computing often consists in a wavefront running over a tiled matrix, involving expensive linear algebra kernels. While these applications might benefit from parallel heterogeneous platforms (multi-core with GPUs), programming wavefront applications directly with high-performance linear algebra libraries yields code that is complex to write and optimize for the specific application. We advocate a methodology based on two abstractions (linear algebra and parallel pattern-based run-time), that allows to develop portable, self-configuring, and easy-to-profile code on hybrid platforms.
Distortion-Free Scanning and Copying of Bound Documents

Despite numerous efforts, the problem of effective correction of perspective and illumination distortions that arise from scanning or copying thick, bound documents, particularly books, remains a very important issue for the development of new image restoration methods and the creation of appropriate hardware. Using a simplified 2D reconstruction requires, in most cases, two-pass scanning and imposes restrictions onto the document types. Nonetheless, the restoration quality seems to be insufficient. In the case of one-pass data capture, the projective distortions remain in the image and there is no reliable way to identify and remove them via further processing due to the absence of mathematical procedures to estimate the surface shape (i.e. image depth) from a single image. The 3D approach described in current chapter is based on obtaining and analysing the form of the book binding areas from photos captured by additional lateral camera(s) that operate during the scanning/copying process. In the beginning, the distances from the platen to the recovered surface are measured via processing of the supporting lateral images. Afterwards, the page length is reconstructed during scanning. Based on the information mentioned above, the distortions are subsequently corrected in following order: (a) perspective distortion, (b) deskewing and relative page turn correction and (c) shadow near binding correction. For the correction of the shadow, the preliminary single calibration of the scanning device is performed. The proposed method allows actualizing one-pass, distortion-free scanning/copying of non-flat documents with minimal changes of existing hardware. The results of the corresponding numerical modelling, as well as processing of the data that was obtained from standard flatbed devices, are given. Considering the latest tendencies in document capture and processing using portable devices, the chapter was extended by the material related to mobile image/document enhancement and the most recent advances in depth extraction from single image using Convolutional Neural Networks (CNN).
Visual Target Tracking Based on Online Discriminant Feature Enhancement

Most of the current similarity algorithms adopt the idea of offline training and online tracking. Firstly, the network is trained offline through a large amount of data, and then the common features of the target are extracted according to the trained network to realize online tracking. Since the learning is a common feature of a large amount of data in the offline training process, different targets have different characteristics. When performing online tracking, the use of common features to express a specific target has its limitations. Therefore, on this basis, this paper increases the online update of the network, and updates the network parameters online, so that the network learns the specific characteristics of the current target and improves the ability of the network to express specific targets. We evaluate the proposed approach on the tracking benchmark: OTB50, OTB100 and VOT2015 dataset. A large number of experimental results show that our algorithm can improve the discriminating ability of deep features and achieve accurate tracking.
Current Situations and Issues of the Mathematics Education in National Institute of Technology, Kure College through the Results of the Attainment Test and the Engineering Mathematics Test

In this paper, we compare Attainment Test for Mathematics and Engineering Mathematics Test (EMaT) carried out by National Institute of Technology, Kure College from 2015 to 2017. We found significant positive correlations between them, implying that academic development of mathematics in Kure College successfully connects to engineering mathematics in university. However, we also found that approximately half of the students do not reach sufficiently high learning level for the engineering mathematics in university. Moreover, differences of scores among four departments are found to be significant. Considering these results, we suggest some educational plans to improve the learning level for the engineering mathematics in university.
Lithuanian traffic monitoring system (LTTMS) based on Android

The number of vehicles is growing exponentially all over the world. Therefore, spreading a real-time information to commuters is necessary. Recently, the GPS availability on smartphones and abundance of license plate recognition algorithms made a mobile phone-based traffic monitoring system highly convenient. In this paper we will introduce a transportation monitoring system for Lithuania. A country which is lacking a reliable traffic application capable of generating traffic data, informing users about the situation on the road and helping to prevent crimes.
A method on path optimization modeling of UAV based on probabilistic model checking

To optimize the design of flight path on unmanned aerial vehicle (UAV for short) and improve its efficiency and safety, a method on path optimization modeling of UAV based on probabilistic model checking is provided. In the proposed method, the problem for the uncertainty of the flight path of UAV is studied, it is caused by arbitrary angle selection and repeated round-trip flight of UAV. When UAV interacts with the operator on duty, the influence of operator's characteristics are considered. In this paper, the scene of road network monitoring by UAV is taken as a case study, accepting the image form UAV, the operator on duty judges whether the image is qualified according to monitoring points and non-monitoring points. Besides, the dangerous area of UAV is avoided and repetitive flight of the initial node in the flight strategy of UAV is limited. Finally, Markov decision process model is extended to specify the formal model for UAV by the target monitoring scene, and multi-objective attributes for UAV is verified by the model checking tool for PRISM. Formal verification results indicate the workload of the operator is reduced, and the efficiency for the operator and the safety of UAV is improved in the proposed method, which provides an excellent way for establishing a safety flight model for UAV.
Multi-Level Web Service Clustering to Bootstrap the Web Service Discovery and Selection

Existing technologies for web services have been extended to give the value-added customized services to users through the service composition. Service composition consists of four major stages: planning, discovery, selection, and execution. However, with the proliferation of web services, service discovery and selection are becoming challenging and time-consuming tasks. Organizing services into similar clusters is a very efficient approach. Existing clustering approaches have problems that include discovering semantic characteristics, loss of semantic information, and a shortage of high-quality ontologies. Thus, the authors proposed hybrid term similarity-based clustering approach in their previous work. Further, the current clustering approaches do not consider the sub-clusters within a cluster. In this chapter, the authors propose a multi-level clustering approach to prune the search space further in discovery process. Empirical study of the prototyping system has proved the effectiveness of the proposed multi-level clustering approach.
Improvement of Cooperative Action for Multi-Agent System by Rewards Distribution

The frequency of natural disasters is increasing everywhere in the world, which is a major impediment to sustainable development. One important issue for the international community is to reduce vulnerability to and damage from disasters. In addition, a large number of injuries occur simultaneously in a large-scale disaster, and the condition of the injured will change over time. Efficient rescue activities are carried out using triage to determine the priority of injury treatment based on the severity of the persons’ conditions. In this chapter, we discuss acquiring cooperative behavior of rescuing the injured and clearing obstacles according to triage of the injured in a multi-agent system. We propose three methods of reward distribution: (1) reward distribution responding to the condition of the injured, (2) reward distribution based on the contribution degree, and (3) reward distribution by the contribution degree responding to the condition of the injured. We investigated the effectiveness of the three proposed methods for a disaster relief problem by an experiment. The results of the experiment showed that agents gained high rewards by rescuing those in most urgent need under the method having the reward distributed according to the contribution degree responding to the condition of the injured.
Research on Chinese Short Text Classification of Bidding Project Names with Fusion Feature Item Category Distribution

Within the framework of word2vec, aiming at the feature of Chinese bidding project names, this paper proposes a TF-IDF-CDW weighted word2vec model, which combines the Category Distribution Weight (CDW) of feature items to generate short text vectors of project names. The short text vector is constructed in three ways, namely the mean word2vec model, the TF-IDF weighted word2vec model, and the TF-IDF-CDW weighted word2vec model. Finally, the three models are applied to the text classification of bidding project names. The experimental results are compared to verify the effectiveness of the new method.
Automatic classification of daily-histograms of solar irradiance in Morocco

This paper presents a study into the utilization of the non-parametric statistic combined with the Bayesian paradigm for solar irradiance conditions clustering. A Dirichlet-multinomial mixture model is applied to automatically adapt to the correct complexity level and size of the model, and therefore, fit complex probability functions. Posterior computation relies on a numerical procedure based on Markov Chain Monte-Carlo algorithm. Experiments were conducted using three years of solar irradiance data carried out in Rabat, capital of Morocco (34°N latitude, 6°50'W longitude) with a sampling-rate of 6-min. The authors of this work are big supporters of open-source codes, therefore, all methods presented in this manuscript is openly available as a downloadable R-package from https://github.com/frimane/SolMultinomClass.
WITHDRAWN: Word n-gram attention models for sentence similarity and inference

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Designing Trajectories in Natural Parameter Curve with Improved Ant Colony Optimization

When a natural parameter curve (NPC) is used for the well trajectory, the curve formulation is a nonlinear transcendental equation set. However, the existing iterative solution method presents such disadvantages as difficult initial value selection, local optima trapping, and low convergence speed. An improved ant colony optimization named IACO is proposed in this study to overcome these advantages effectively. Firstly, a nonlinear constraint equation with an objective function for designing an NPC trajectory is established to minimize the sum of the squared deviations of borehole trajectory coordinates. The ranges of well measured depth increment, inclination angle, and azimuth angle are used as constraints. Secondly, by using the unequal length discretization method to mesh the continuous space of constraints and objective function difference as the heuristic function to improve the ACO to solve the nonlinear constraint equation. Finally, through a case verification shows that the proposed IACO exhibits good convergence speed and accuracy and can provide engineering designers with a scientific and effective computation method.