Reviewerly/exHarmony huggingface dataset:
Big Data Analysis on Social Networking

Social networking media, such as Twitters, Facebook, and Chinese Weibo, has become a major means for people to deliberately express their ideas, thoughts, and views about everything. A huge amount of posts in these social media are issued and viewed by the general public daily. Evidently the social networking media can directly affect people's perceptions on a specific topic. Those data can be used to valuable information that will help organizations to understand what thetrends or sentiments are. Most of the research efforts in social networking data analytics are conducted in English-based social networking media. Research on Chinese social networking media receives relatively little attention. In this paper, we examine the key problems in this field, focus particularly on the characteristics of new vocabulary, emotion expressions, and hierarchical structures in Chinese “Weibo”. Associated theoretical and technological methods to address these problems are reviewed and discussed.
A New Big Data Model Using Distributed Cluster-Based Resampling for Class-Imbalance Problem

Abstract The class imbalance problem, one of the common data irregularities, causes the development of under-represented models. To resolve this issue, the present study proposes a new cluster-based MapReduce design, entitled Distributed Cluster-based Resampling for Imbalanced Big Data (DIBID). The design aims at modifying the existing dataset to increase the classification success. Within the study, DIBID has been implemented on public datasets under two strategies. The first strategy has been designed to present the success of the model on data sets with different imbalanced ratios. The second strategy has been designed to compare the success of the model with other imbalanced big data solutions in the literature. According to the results, DIBID outperformed other imbalanced big data solutions in the literature and increased area under the curve values between 10 % and 24 % through the case study.
A method and software solution for classifying clast roundness based on the radon transform

In this paper, an algorithm for clast roundness classification based on the Radon transform is presented. The degree of roundness is determined by processing the sinogram of the clast image. The algorithm consists in applying two low-pass filters to the sinogram, obtaining the inverse Radon transform and comparing the filtered images with the original image. For rounded particles, the difference between the original image and either of the filtered images will be small. For angular clasts, the difference will be greater than for rounded clasts, due to the presence of high-frequency components. In the comparison process, each of the two filtered images are subtracted from the original image to yield two difference images. Since the data are binary, these two images present topologically unconnected regions that correspond to the particle's edges. The percentage of non-overlapping area between the original and the difference images, and the number of regions are used to classify the morphology of the clast. The results have been validated using a comparison chart designed for visual roundness estimation. The comparison chart, consisting of five roundness classes, was proposed by Russell, Taylor and, Pettijohn (Müller, 1967). Two cutoff frequencies, one to classify well-rounded, rounded and sub-rounded clasts and another for angular and sub-angular classes, were used. The proposed algorithm correctly classifies the roundness classes of the visual graph. The results provided by the algorithm were compared with the classification performed by a group of experts. The algorithm assigned 92% of the clasts to the same classes as the human experts. We also propose Gaussian models, which are useful to classify the particles into the five classes. We have developed a user-friendly software to carry out the roundness classification algorithm. This software was developed on the MATLAB platform and can be freely downloaded from the public repository.
A New Application for Gabor Filters in Face-Based Gender Classification

Human face is one of the most important biometrics as it contains information such as gender, race, and age. Identifying the gender based on human face images is a challenging problem that has been extensively studied due to its various relevant applications. Several approaches were used to address this problem by specifying suitable features. In this study, we present an extension of feature extraction technique based on statistical aggregation and Gabor filters. We extract statistical features from the image of a face after applying Gabor filters; subsequently, we use seven classifiers to investigate the performance of the selected features. Experiments show that the accuracy achieved using the proposed features is comparable to accuracies reported in recent studies. We used seven classifiers to investigate the performance of our proposed features. Experiments reveal that k-Nearest Neighbors algorithm (k-NN), K-Star classifier (K*), and Rotation Forest offer the best accuracies
Investigating potential icequakes at Llaima volcano, Chile

Glacially- and magmatically-derived seismic events have been noted to heavily overlap in characteristics, thus there exists the potential for false-alarms or missed warnings at ice-covered volcanoes. Here we present the first study to specifically target icequakes at an ice-covered volcano in Southern Chile. Two months of broadband seismic data collected at Llaima volcano in 2015 were analyzed in order to quantify, characterize, and locate glacially-derived seismic events at one of the most active ice-covered volcanoes in the region. We find over 1,000 repeating seismic events across 11 families, the largest of which contains 397 events. Approximate locations and characteristics of the largest families lead us to conclude that these events were derived from persistent stick-slip motion along the ice-rock interface at the base of a glacier near the volcano summit. These results have implications for future seismic monitoring at Llaima volcano and other ice-covered active volcanoes in the region.
Hierarchies of localizable entanglement due to spatial distribution of local noise

Complete characterization of a noisy multipartite quantum state in terms of entanglement requires full knowledge of how the entanglement content in the state is affected by the spatial distribution of noise in the state. Specifically, we find that if the measurement basis in the protocol of computing localizable entanglement and the basis of the Kraus operator representing the local noisy channel do not commute, the information regarding the noise is retained in the system even after the qubit is traced out after measurement. Using this result and the basic properties of entanglement under noise, we present a set of hierarchies that localizable entanglement over a specific subsystem in a multiqubit state can obey when local noise acts on the subparts or on all the qubits of the whole system. In particular, we propose two types of hierarchies---one tailored according to the number of noisy unmeasured qubits, and the other one that depends additionally on the cardinality of the set of noisy measured qubits, leading to the classification of quantum states. We report the percentage of states satisfying the proposed hierarchies in the case of random three- and four-qubit systems and show, using both analytical methods and numerical simulations, that in almost all the cases, anticipated hierarchies tend to hold with the variation of the strength of noise.
SASGBC

Spoken language understanding (SLU) involves two tasks, namely slot filling and intent prediction. While the goal of slot filling is to predict correlated slot sequences, intent prediction aims to capture the intent for the whole utterance. Recent work shows that jointly learning both tasks can provide additional benefits. In this paper, we follow this line of work and explore the pre-trained BERT model for SLU. We find that BERT suffers from problems in learning logical dependency relation for slot filling. To address this issue, we propose a new joint learning model called SASGBC that combines sequence labeling with deep bidirectional Transformer. Experiments on two datasets demonstrate that SASGBC achieves state-of-the-art performance compared to several competitive baseline models.
Design Principles for Explainable Sales Win-Propensity Prediction Systems

MAN Energy Solutions, one of the largest ship engine manufacturers in the world, is looking into further improving its hit rate of through-life engineering services and spare parts quotations. We help to solve this relevant field problem by building a novel machine learning based sales win-propensity prediction system that utilizes the lightGBM algorithm, SHapley Additive exPlanations, and a second layer conditional probability model of quotation age. Moreover, we build an implementation method for the broader class of such systems and extend the scientific literature on explainable machine learning by abductively developing and instantiating the design principles (DPs) of local contrastive explainability, global explainability, selective visualization, causality, confirmatory nudging, and accountability in a sales win-propensity system.
Prediction of network security based on DS evidence theory

Network security situation prediction is difficult due to its strong uncertainty, but DS evidence theory performs well in solving the problem of uncertainty. Based on DS evidence theory, this study analyzed the prediction of the network security situation, designed a prediction model based on the improved DS evidence theory, and carried out a simulation experiment. The experimental results showed that the improved method could predict accurately in the case of a large conflict, and had strong anti-jamming abilities as compared with the original method. The experimental results prove the effectiveness of the improved method in the prediction of the network security situation and provide some theoretical basis for the further application of DS evidence theory.
C4.5 Algorithm with Average Gain to Predict Human Development Index in Indonesia

Poorness makes the government's program to create national development goal rising -Benchmark for the success of human development called Human Development Index.An area that has a high Human Development Index should have good quality human, or if the Human Development Index high, then grade of the poorness is low.C4-5 algorithm method is one of the methods of creating a decision tree from the training data available.C4-5 algorithm is the expansion from ID3.The goal of this research is to predict the Human Development Index in Indonesia and make the decision tree from the data.Attributes that use in this research are Life Expectancy, Mean Years of Schooling, expect years of schooling, and per capita income.There are two an in this research are with mean and median from each attribute.The data for this research method 4473 data.The result shows that Scenario with the has classification is 1st Scenario with mean from each attribute, that to accurate 85,893%.
The Optimization Technique for Joint Discrete Search and Detection of Observation Objects

Briefly analyzes the main results of solving problems of search and detection of objects of observation in information systems.A discrete search zone is introduced.The task is to find the optimal Bayes decision rule in the current discrete search zone.The Bayesian decision-making rule is clarified when jointly optimizing discrete search and detection of observation objects.
Entanglement-Assisted Capacity of Quantum Channels with Side Information

Entanglement-assisted communication over a random-parameter quantum channel with either causal or non-causal channel side information (CSI) at the encoder is considered. This describes a scenario where the quantum channel depends on the quantum state of the input environment. While Bob, the decoder, has no access to this state, Alice, the transmitter, performs a sequence of projective measurements on her environment and encodes her message accordingly. Dupuis established the entanglement-assisted capacity with non-causal CSI. Here, we establish characterization in the causal setting, and also give an alternative proof technique and further observations for the non-causal setting.
A Unifying Framework for Human–Agent Collaborative Systems—Part I: Element and Relation Analysis

The human-agent collaboration (HAC) is a prospective research topic whose great applications and future scenarios have attracted vast attention. In a broad sense, the HAC system (HACS) can be broken down into six elements: "Man," "Agents," "Goal," "Network," "Environment," and "Tasks." By merging these elements and building a relation graph, this article proposes a systematic analysis framework for HACS, and attempts to make a comprehensive analysis of these elements and their relationships. We coin the abbreviation "MAGNET" to name the framework by stringing together the initials of the above six terms. The framework provides novel insights into analyzing various HAC patterns and integrates different types of HACSs in a unifying way. The presentation of the HACS framework is divided into two parts. This article, part I, presents the systematic analysis framework. Part II proposes a normalized two-stage top-level design procedure for designing an HACS from the perspective of MAGNET.
Hurdle: Securing Jump Instructions Against Code Reuse Attacks

Code-reuse attacks represent the state-of-the-art in exploiting memory safety vulnerabilities. Control-flow integrity techniques offer a promising direction for preventing code-reuse attacks, but these attacks are resilient against imprecise and heuristic-based detection and prevention mechanisms.
New Bayesian Single Index Quantile Regression Based on Uniform Scale Mixture

To scale back the dimensionality while holding a lot of flexibility of a nonparametric model Wu, et al. (2010) proposed a single index conditional quantile regression model. In this paper, a new Bayesian lasso for single index quantile regression model is proposed based on a scale mixture uniform. In addition, we construct an efficient and sampling Gibbs algorithm for posterior inference based on a uniform scale mixture representation for Laplace distribution. Simulation study have considered to evaluate our proposed method compare to the existing methods. The results of simulations indicate that the new Bayesian algorithm performs well.
Real-time Ship Track Association: a Benchmark and a Network-Based Method

Multiple systems are tracking ships independently, including cooperative as well as non-cooperative ones. Most track association works focused on association cost functions and optimal track matching matrix was determined by solving a multidimensional assignment problem, assuming synchronous detections of fixed number of targets received periodically from an ideal communication channel, which is not satisfied when tracking targets in a large scale by heterogeneous methods. We put forward an evolving target network-based association approach to create, update, merge, separate and destroy targets over time, which centers on targets rather than tracks. In the network, nodes are fused ships corresponding to tracks from one or more sources and weights of directed edges pointing to each node are association beliefs constituting its basic probability assignment (BPA) function in Dempster-Shafer theory (DST). Once a new message is received, this network evolves by combining the corresponding node&#x0027;s BPA with a new one calculated according to the message. This method is evaluated by an extensible benchmark consisting of an open source track simulator ICTShips and a free visualization software ICTShipVisual. The benchmark consists of four scenes with 32 to 102,231 targets observed by 3 sources at present, having GERs (Gap Error Ratio) from 0.787 to 1,412. Error ratios of our track association method under these scenes range from 0&#x0025; to 0.539&#x0025;.
An Aposteriorical Clusterability Criterion for k-Means++ and Simplicity of Clustering

Abstract In this paper, the notion of a well-clusterable data set is defined combining the point of view of the objective of k -means clustering algorithm (minimizing the centric spread of data elements) and common sense (clusters shall be separated by gaps). Conditions are identified under which the optimum of k -means objective coincides with a clustering under which the data is separated by predefined gaps. Two cases are investigated: when the whole clusters are separated by some gap and when only the cores of the clusters meet some separation condition. A major obstacle for using known clusterability criteria is their reference to the optimal clustering which is NP hard to identify. In this paper, this obstacle is overcome. Compared to other approaches to clusterability, the novelty consists in the possibility of an a posteriori (after running k -means) check if the data set is well-clusterable or not. As the k -means algorithm applied for this purpose has polynomial complexity so does therefore the appropriate check. Additionally, if k -means++ fails to identify a clustering that meets clusterability criteria, with high probability the data is not well-clusterable.
Assistive Systems for Special Needs in Mobility in the Smart City

The Smart City is a multifaceted objective aiming at increasing viability of the city for its inhabitants. Mobility in the city is an important dimension of the Smart City for humans and goods, which can either move without assistance or may need special assistance. The objective of this paper is to investigate the role of assistive systems for the mobility of humans and goods in order to take into account special needs. We discuss and classify different situations for assistance of humans and goods either by means of technology or humans (and technology) and present different assistive system behaviors already used or to be implemented in the future.
Design and evaluation of an adaptive framework for virtual learning environments

Traditional virtual learning environments may not always be suitable as they overlook the diverse requirements of students and are designed generally to support certain learning activities.Adaptivity is often proposed as a promising solution to overcome that limitation.However, it is still challenging to find the proper way to design such systems in order to adapt learning material in accordance with the students' characteristics.This paper, therefore, provides an adaptive framework to design different instances of adaptive virtual learning environments.An implementation based on the proposed framework resulting in an adaptive virtual learning environment is also presented.The adaptive environment incorporates learning style and student performance.These two student characteristics are used to produce personalized learning paths as the main adaptive feature.An illustrative example is also offered to highlight how the framework can be used and implemented.The paper also presents an evaluation of the developed adaptive virtual learning environment in terms of perceived usefulness and learning engagement.A controlled experiment was managed with seventy-five participants in a learning environment.The results indicate that the adaptive virtual learning environment can be better to support students in terms of their perception and better in engaging them in the learning process than when they interact with a non-adaptive version.The framework can be valuable as a foundation in designing such systems and in enhancing future adaptive online-learning research.Future directions of research are also highlighted.
Toward Blockchain Technology in IoT Applications: An Analysis for E-health Applications

Research involving Blockchain technology has been growing due to its decentralization and immutability characteristics. Several applications from the most varied areas can benefit from the Blockchain, such as finance, logistics, and others. Internet of Things (IoT) is a new paradigm that has emerged to provide services to users through intelligent objects arranged in the day-to-day and provided various research, for example, smart buildings, smart cities, precision agriculture, and E-health. E-health applications can be considered as a set of computational solutions focused on the health area. This paper aims to study the introduction of Blockchain technology in an E-health approach. A Blockchain tool was used in conjunction with an E-health database and evaluated its performance. The results indicated that the transaction validation times are favorable for the characteristics of an E-health application.
Queens in Exile: Non-attacking Queens on Infinite Chess Boards

Number the cells of a (possibly infinite) chessboard in some way with the numbers $0, 1, 2, \ldots$. Consider the cells in order, placing a queen in a cell if and only if it would not attack any earlier queen. The problem is to determine the positions of the queens. We study the problem for a doubly-infinite chessboard of size $\mathbb{Z} \times \mathbb{Z}$ numbered along a square spiral, and an infinite single-quadrant chessboard (of size $\mathbb{N} \times \mathbb{N}$) numbered along antidiagonals. We give a fairly complete solution in the first case, based on the Tribonacci word. There are connections with combinatorial games.
Performance Evaluation of Symmetric Data Encryption Algorithms: AES and Blowfish

People are transferring large amount of data that are critical and consume large amount of time through the Internet such as email, banking transaction and online purchase.But due to high exposure they are susceptible to being heavily attacked or become attractive targets for attackers.This can be solved by using a modern phenomenon called symmetric encryption.Symmetric encryption is used to ensure that information is hidden from anyone for whom it is not intended.This research conducted a performance evaluation of symmetric data encryption algorithms namely Advanced Encryption Standard (AES) and Blowfish.The evaluation is done for four different data types: image data type, audio data types, video data types and textual file data types.The performance evaluation metrics are encryption time and throughput.The prototype is developed using JAVA, compiled using the Netbeans IDE7.1.2with default settings in jdk 7.1 development kit.Results obtained from this evaluation indicated that blowfish is more efficient than AES.But for Blowfish the encryption time sometime decreases with the increase in data size.This can be attributed to the fact that Blowfish uses 126, 192 or 256 key sizes.