Reviewerly/exHarmony huggingface dataset:
Network Forensics in GSE Overlay Networks

The importance of captured network traffic as a data-source for law enforcement crime investigation has increased because many devices are Internet-enabled and the data communication might yield crucial evidence for an investigation. There are many points in the Internet Service Provider's infrastructure where the network traffic might be captured. One of them is a satellite connection, DVB-S2, which use Generic Stream Encapsulation (GSE) protocol that carries IP traffic. Current tools for network traffic forensic analysis do not support GSE. In this paper, we describe principles of GSE, methods for GSE traffic analysis and the extension for an existing network forensic tool that performs GSE traffic processing and extraction of encapsulated communication.
Online social networking usage pattern of introvert and extrovert adolescent girls

Online social networking has changed the way the adolescents live, socialize and share.Social networking sites have provided a new platform for individuals to communicate and maintain existing relationship, share information and keep oneself updated with various current affairs in which one's personality may play an important role.The present study was undertaken to explore and compare the usage pattern of online social networking of 60 extrovert and introvert adolescent girls.Introversion-Extroversion inventory was utilized for sample selection.An inventory on online social networking was developed and utilized to assess the usage pattern of online social networking of extrovert and introvert adolescent girls.Frequency, percentage and ttest were computed for analysis of data.The results of the study revealed that usage pattern of online social networking was almost similar with little difference in few cases such as majority of extrovert girls relied on mass-media for getting information about social networking sites and spent less time per day on social networking sites than introvert girls.No significant difference was observed among introvert and extrovert girls in usage pattern of online social networking.
An Economic Analysis of Cloud Computing Service Using Reclaimed Resources

Resource under-utilization is prevalent in data centers as a result of substantial stochastic workload variations and resource provision guarantee for the requirements to provide high-level service availability. To avoid resource under-utilization, recently, the Economy cloud computing service class (Economy class in short) is proposed that sells allocated but unused (i.e., reclaimed) physical resources with long-term service level objectives (SLOs). Though the Economy class would lead to revenue benefit, how much of the reclaimed resources should be used as the Economy class (i.e., capacity planning) and how to set its price and SLAs to maximize revenue benefit have not been studied. To address this problem, in this paper, we propose a multi-level SLA for Economy class, which has multiple levels of SLOs and penalty. We then present a rigorous theoretical analysis on the capacity planning and pricing of the new Economy class with an objective to maximize the revenue of the cloud computing service provider. Theoretical results are validated using resource utilization data derived from a publicly available Google cluster workload trace dataset.
MLModelScope: Evaluate and Introspect Cognitive Pipelines

The current landscape of cognitive pipelines exercises many Machine Learning (ML) and Deep Learning (DL) building blocks. These ML and DL building blocks leverage non-uniform frameworks, models, and system stacks. Currently, there is no end-to-end tool that facilitates ML and DL building blocks evaluation and introspection within cognitive pipelines. Due to the absence of such tools, the current practice for evaluating and comparing the benefits of hardware or software innovations on end-to-end cognitive pipelines is both arduous and error-prone - stifling the rate of adoption of innovations. We propose MLModelScope: a hardware/software agnostic platform to facilitate evaluation and introspection of cognitive pipelines in the cloud or on the edge. We describe the design and implementation of MLModelScope and show how it provides a holistic view of the execution of components within cognitive pipelines. MLModelScope aids application developers in experimenting with and discovering cognitive models, data scientists in comparing and evaluating published algorithms, and system architects in optimizing system stacks for cognitive applications.
Study on Image Mosaic Algorithm for Crack Detection of Bridge Surface

The surface cracks of bridge building will seriously effect its service life. In order to meet the requirements of actual engineering inspection, this paper studies and improves the large-area mosaic algorithm for multiple images of bridge crack. The algorithm combines the phase correlation method to estimate the overlapped regions of adjacent images. The feature points of the overlapped regions are extracted by the SIFT algorithm based on adaptive contrast threshold, and the image registration is completed by the improved RANSAC algorithm. Then these sequential images are respectively mapped into the same reference plane by projection transformation, and gradually blended by improved weighted fusion algorithm. It is used to achieve a splicing panoramic image of the complete bridge crack with wide viewing angle and high resolution. The proposed algorithm of large-area image mosaic is verified by the experiment processing and performance analysis, which can reduce the search of non-essential feature and achieve the seamless splicing with reliable precision. The comparison of instance results shows that the correct matching rate of feature points is as high as 97.1%, and the mosaic efficiency is improved by more than 35.2% compared with the conventional SIFT image mosaic algorithm.
Personalized Recommendation with User Categories of Different Behavior Modes

As resources of E-commerce platform grow exponentially in recent years, how to filter and recommend the items that users are interested in from the massive resources has become an increasingly concerned topic in both academia and industry. This paper, based on the users and sales data of Taobao, the biggest E-commerce platform in China, studies the personalized recommendation in the e-commerce scenario. With users' behavior data, this paper first identifies different user groups with different shopping behavior modes. Secondly, recommendation strategies are established for the user group with a low purchase rate. Thirdly, item-based collaborative filtering and user-based collaborative filtering are applied to user groups with different degrees of personalized requirements. The experimental results show that the recommendation algorithm based on different user categories can better adapt to the users with different shopping behavior modes.
Youth Perception of Civic Literacy-Based Education in Millennial Era

Millennial era is closedly associated to the rapid growth of information and communication technology. This phenomenon becomes concerning because of the negative effects it might cause in variety aspects of life, such as people's way to communicate and to socialize. Thus, this study aims to investigate how the youth perceives civic literacy-based education in millennial era. Descriptive analysis with qualitative approach is employed as the method of the study. The result shows that it is absolute for the young generation as the successor of the nation to possess basic knowledge about civic literacy as a fundamental guidance to block off negative influences from information and communication technology in this millennial era.
Supervised High-Dimension Endecoder Net: 3D End to End Prediction Network for Mark-less Human Pose Estimation from Single Depth Map

Most of the existing deep learning-based methods for Mark-less human pose estimation from a single depth map are based on a common framework that takes a 2D depth map and directly regresses the 3D coordinates of human body joints via 2D convolutional neural networks (CNNs). But depth map is intrinsically 3D data, treat it as 2D images will distort the shape of the actual object through projection from 3D to 2D space, and compels the network to perform perspective distortion-invariant estimation. Moreover, directly regressing 3D coordinates from a 2D image is a highly nonlinear mapping, which causes difficulty in learning procedure. To overcome these problems, a module called Supervised Endecoder is proposed to process 3D convolution data, which can also be stacked through series connection to adapt different size of dataset. Based on the module, a network called Supervised High Dimension Endecoder Network is designed, which can be used to predict key points of markless human in a single depth map in 3D space. Experiments show improved prediction accuracy compared to the state-of-the-art approaches.
Triplet Transform Learning for Automated Primate Face Recognition

Automated primate face recognition has enormous potential in effective conservation of species facing endangerment or extinction. The task is characterized by lack of training data, low inter-class variations, and large intra-class differences. Owing to the challenging nature of the problem, limited research has been performed to automate the process of primate face recognition. In this research, we propose a novel Triplet Transform Learning (TTL) model for learning discriminative representations of primate faces. The proposed model reduces the intra-class variations and increases the inter-class variations to obtain robust sparse representations for the primate faces. It is utilized to present a novel framework for primate face recognition, which is evaluated on the primate dataset, comprising of 80 identities including monkeys, gorillas, and chimpanzees. Experimental results demonstrate the efficacy of the proposed approach, where it outperforms the existing approaches and attains state-of-the-art performance on the primates database.
Application of the multi-attribute anomaly model for prospecting potential at depth: A case study of the Haiyu Au deposit in the Jiaodong Gold Province, China

Exploration geochemistry has become an indispensable tool in the discovery of certain types of mineral deposit. The multi-attribute anomaly model, as a geochemical exploration tool for prospecting potential at depth, is widely applied to indicate the enrichment and depletion of multi-elements coexisting in certain types of deposits. In the Haiyu gold deposit, the system mass-transfer μ anomaly (μ > 0.15) represents the zones affected by element exchange and fluid migration during metallogenic processes. Values of ΔCNaO-A < −20 kg and μNa < −50% represent fluid activity zones, while ΔCSO–A > 1000 g and ΔCNaO-A < −20 kg suggest the existence of mineral sulfides. The correlations among S-FeO-Au indicate fertile rocks and barren rocks in different locations. The positive anomalies of pathfinder elements indicate the occurrence of numerous polymetallic sulfides and the enrichment of multi-elements coexisting in the Haiyu gold deposit. The deeper and larger multi-attribute anomalies in the No. 30 profile of the Haiyu Au deposit indicate that the prospecting potential at depth mainly focuses on the extension of the footwall along the NE-NNE Sanshandao-Cangshang Fault (F3). In summary, the Haiyu Au deposit has great prospecting potential for additional deep-seated orebodies.
Robust Picture Search by Absent Color Indexing

Picture search has become a vital part of image processing where color histogram-based methods can effectively identify objects. However, conventional color histogram-based methods are not sensitive to similar objects. This paper aims to propose a novel scheme for robust picture search based on Absent Color Indexing, ABC in short. It generates two-dimensional color histograms via CIE Lab color space. Then, we utilize a threshold \( h_{T} \) to divide them into an apparent color histogram and another absent color histogram. To enhance the performance by absent colors, we invert absent color histograms. Finally, we calculate similarity by histogram intersection. Experimental results on Mondrian random patterns and real-scene pictures show that the proposed ABC method is more robustness than conventional color histogram-based methods. Especially for similar searching objects, it has a large margin to distinguish different objects.
Swarm Inspired Approaches for K-prototypes Clustering

Data clustering is a well-researched area in data mining and machine learning. The clustering algorithms that can handle both numeric and categorical variables have been extensively researched in the recent years. However, the clustering algorithms have a major limitation that converge to a local optima. Therefore, to address this problem this paper has proposed a novel algorithm ABC k-prototypes (Artificial Bee Colony clustering based on k-prototypes) for clustering mixed data. In our proposed approach we use the combination between the distribution centroid and the mean to calculate the dissimilarity between data objects and prototypes. The proposed algorithm is tested on five different datasets taken from the UCI machine learning data repository. The comparative results in the performance measures of the clustering showed that the proposed algorithm outperformed the traditional k-prototypes.
Research on Cable Crane Lightning Protection for Hydroelectric Power Station Construction

The construction of the hydroelectric power station is a large civil engineering. The cable cranes in which the construction of the hydroelectric power station are mainly to transport concrete from high places to the dam, and the construction period is usually several years. The cable cranes are located at the high place of the construction site, including control and communication electronic equipment. In addition, the grounding resistance of the cable cranes is often high, which is easily hazarded by lightning stroke, thus affecting the progress and safety of the dam construction. However, lightning protection for cable crane is not fully considered and lack of relevant standard at present. In this paper, the structure and environmental characteristics of the cable cranes on construction site of hydroelectric power stations are investigated. The lightning protection zone of the cable crane is established according to IEC 62305-2010 (GB/T 21714-2015 equivalent Chinese version). The external lightning protection system and the internal surge protection measures of the cable cranes are designed. This lightning protection retrofit plan has been applied to several hydroelectric power station construction projects, such as Zangmu, Xiluodu, Xiangjiaba and Wudongde hydroelectric power Station, preventing a large number of lightning accidents of cable cranes and ensuring the safety and construction progress of the projects.
Comparative Analysis of effective factors on Formation and Development of Academic Spin off in two Ministries of Science and Health

Comparative Analysis of effective factors on Formation and Development of Academic Spin off in two Ministries of Science and Health
Opcount: A Pseudo-Code Performance Estimation System for Pairing-Based Cryptography

We propose a simple framework for evaluating the performance of pairing-based cryptographic schemes for various types of curves and parameter settings. The framework, which we call ‘Opcount’, enables the selection of an appropriate curve and parameters by estimating the performance of a cryptographic scheme from a pseudo-code describing the cryptographic scheme and an implementation-information database that records the performance of basic operations in curves targeted for evaluation. We apply Opcount to evaluate and compare the computational efficiency of several structure-preserving signature schemes that involve tens of pairing products in their signature verification. In addition to showing the usefulness of Opcount, our experiments also reveal the overlooked importance of taking account of the properties of underlying curves when optimizing computations and demonstrate the impact of tight security reductions.
A Cockpit of Measures for Image Quality Assessment in Digital Film Restoration

We present an alternative approach for the quality assessment of the digital restoration of a degraded film. Instead of summarizing the film quality by an unique value, here we propose a set of basic measures that account for different film visual features. These measures describe over time global and local properties of the film, like brightness, contrast, color distribution entropy, color variations and perceptual intra-frame color changes. They are relevant to estimate the level of readability of the visual content of the film and to quantify the perceptual differences between the original and restored film. The measures proposed here are viewed as the parameters showed in a car or airplane cockpit and necessary to control the machine status and performance. The idea of cockpit would like to contribute to the automation of the digital restoration process and of its evaluation that are currently still performed for the most part manually by video editors and curators and thus often biased by subjective issues.
The Extended Authentication Process in the Environment of the Laboratory Information and Management System

Laboratory information systems have evolved from applications designed to simplify or eliminate routine processes. Their further development follows the trend of interconnection with intelligent laboratory equipment. Laboratories of primary research are specific in many ways and commonly available information systems cannot be deployed there. Development of a new lab information and management system should be designed in order to ensure not only safe and secured operation but also modular requirements. Elements of data security can be successfully implemented on the basis of studies and standards which deal with this issue on a general level. The area of security should be viewed in the context of the lab as the whole. The mentioned type of laboratories processes a sensitive material, creates compounds of nonresearched properties, which are studied and results are subsequently published. It is obvious that the environment has to be secured at all levels. The following article describes a possible solution.
Detection and Extraction Features for Signatures Images via Different Techniques

Abstract Signature is one of the most important features to identify individuals. It represents a specific mark that includes handwritten characters or symbols. Also, signing takes place in a wide range of businesses, such as bank transactions and government documents so it provides a good way to maintain security, in biometric systems. Signature is used as a feature to identify the user by extracting a set of features. Over time, a number of techniques have been developed to identify and extract a set of features from the signature image. Although there are many of these techniques, there is a set of elements that determines the feasibility of using a particular technique, such as accuracy, computational complexity, and the time needed to extract features. In this paper, three widely used feature detection algorithms, SURF, BRISK and FAST, these algorithms are compared to calculate the processing time and accuracy for set of signatures correctly. Three techniques have been applied using (UTSig) dataset; the results showed that the BRISK algorithm got the best result among the feature detection algorithm in terms of accuracy and the FAST algorithm got the best result among the feature detection algorithm in terms of run time.
Cryptanalysis of Reduced-Round SipHash

Abstract SipHash is a family of ARX-based MAC algorithms optimized for short inputs. So far, a lot of implementations and applications for SipHash have been proposed, whereas the cryptanalysis of SipHash still lags behind. In this paper, we study the property of truncated differential in reduced-round SipHash. By exhaustively testing all kinds of 1-bit input differences, we find out the greatest differential biases from corresponding output bits through 3 or 4 SipRounds. Making use of these results, we construct distinguishers for SipHash-2-1 and SipHash-2-2 with practical complexities of $2^{12}$ and $2^{36}$, respectively. However, one limitation of the latter is that it begins with 1-bit input differences on the most significant message bit, which means it can only work when neglecting the padding rules of SipHash. Furthermore, we reveal the relations between the value of output bias and the difference after the first modular addition step, which is directly determined by corresponding key bits. Based on these relations, we propose a key recovery method for SipHash-2-1 that can obtain a significantly nonuniform distribution of the 128-bit secret key. It is summarized that about $97\%$ of random keys can be fully recovered under this method within a complexity of $2^{83}$.
Uyghur Text Detection in Natural Scene Images

Text detection in natural scene images is a challenging task for many content-based image analysis missions. Actually, widely-used methods mainly focus on commonly used languages, while for the Uyghur language of minority, text detection is paid less attention. In this paper, we propose an effective and accurate Uyghur text detection method based on Sobel edge detection algorithm. Binarization, morphology operation and connected components analysis are used to detect Uyghur text regions successively. In addition, location of Uyghur text was realized by text regions merging. The proposed algorithm is evaluated on 4 representative groups of natural scene images. The True Positive Rate (TPR) of each picture is over 0.78 by our method which is better than the Maximally Stable Extremal Regions (MSER) method and the texture based method. Experimental results show that for natural scene images, Uyghur text detection and location can be obtained by proposed method.
Web-Based Comprehensive Assessment for Postpartum Care using Rule-Based Algorithm

Abstract Administration of postpartum care has been performed in line with health care standards. Unfortunately the health service medical record data is still incomplete.This results in delays in establishing a diagnosis and providing therapy which results in a worsening of the postpartum mother’s condition.This paper proposes a web-based information system that may become one solution in solving the remaining issues in postpartum care. The system developed here is expected to improve health care quality with better data completion of postpartum mothers and access to them. The system developed here is web-based, in that it applies the rule-based artificial intelligence system. Input of the system are results of physical examination performed by medical professionals. Then processed using the rule-based algorithm to produce diagnosis and proper recommendation. This research is quasi-experimental using posttest only with control group. There were 40 were categorized into intervention group (n=20) who used the information system and control group (n=20) who used the present system endorsed by the government.. The rule-based algorithm in the information system uses validation from experts. Results show that the information system developed here is capable of solving the issue of incomprehensive data, as well as effectiveness in providing postpartum care.