Reviewerly/exHarmony huggingface dataset:
A Partition Dirichlet Process Model for Functional Data Analysis

Recently, extensions of the Dirichlet process to the functional domain have been presented in the literature. These processes can be classified based on the type of labeling they induce across different arguments in the domain, i.e., marginal or joint labeling. We show that marginal labeling processes have undesirable properties if functional observations are assumed to be almost surely continuous. Joint labeling processes avoid this undesirable behavior. They are specified through finite dimensional distributions for locations across the entire domain. They range from a common label for all arguments (relatively easy to fit) to joint local label selection at every argument (computationally very demanding). Here, we offer a middle ground - a joint labeling process which partitions the domain of the stochastic process and assign labels to individual partition elements. We call the proposed model a partition functional Dirichlet process and show that it can outperform both of the foregoing extremes of joint labeling. Given data that is a sample from each function in a collection of independent functions over the given domain, we employ this process as a prior for the true functions. We show results from simulation studies as well as a dataset arising from reflectance curves to demonstrate the performance of this process and make comparison with the two extreme labeling models.
New Approach in the Rainbow Tables Method for Human-Like Passwords

This paper represents a new approach to rainbow tables, a method of password recovery that was originally developed by Martin E. Hellman and then improved by P. Oechslin, so most of its implementations use Oechslin's modification. An improvement represented in this work mostly lies in the reduction function, which uses character statistics to generate more "human-like" passwords. Though it generates passwords 5 to 10 times slower than reduction function, which uses direct dependency between hash bytes and the inserted characters, it significantly increases common efficiency in memory (8 to 30 times less memory needed to store these tables) and successful "human-like" passwords recovery probability, while these tables are generated by the same time as tables with the use of "random" reduction function.
Preliminary Study on the Characteristics of Ductile Shear Zone Type Gold Deposit

Abstract The ductile shear zone type gold deposit is a very important type of gold deposit. For the study of metallogenic characteristics of ductile shear zone, the development is extremely rapid, and there are already quite mature research methods and relatively complete research contents. This paper mainly focuses on the structural characteristics, fabric characteristics of the shear zone, the relationship between the deformation of the shear zone and mineralization, the relationship between gold deposits and shear zones, the geochemical characteristics of the shear zone gold deposits, the metallogenic model and the future. The research direction has been made a simple narrative.
End-to-End Verifiable Quadratic Voting with Everlasting Privacy

Quadratic voting is an intriguing new method for public choice suggested by Lalley and Weyl, which they showed to be utilitarian efficient. Voters are given a budget of credits and can assign each of the candidates a (perhaps negative) value, where the price paid for their voting choice is the sum of the squared values. From a security viewpoint, we generally request elections to be private and have integrity, and even further (end-to-end) verifiability which entails public bulletin boards. Such public data might be troublesome when considering future adversaries capable of breaking current cryptographic primitives, either due to computational power advances, broken primitives or scientific breakthroughs. This calls for election schemes with everlasting privacy and perfectly private audit trails. In the case of quadratic voting this is even more crucial since budget balances have to be linked between elections in a verifiable way, and revealing old budget values partially break privacy in later elections. In this paper, we suggest an efficient construction of electronic quadratic voting with end-to-end verifiability and a perfectly private audit trail inspired by the methods of Cuvelier, Pereira and Peters, but adapted to include the quadratic relations and keeping budget balances everlasting private.
Lightning Detection Technology and Application

This paper introduces the basic electromagnetic wave principle of VLF / LF lightning detection technology, explains the time of arrival method (TOA) and magnetic orientation method (DF) used in lightning location instrument in detail, and gives the method of cloud flash location according to the time-differences method, introduces the basic characteristics of recording the ground flash waveform and cloud flash waveform, and provides the independent method of Beijing Huayun Orient Detection Technology Co., Ltd. The typical cloud to ground lightning waveform and cloud to ground lightning waveform samples collected by ddw1 lightning location instrument in nature are introduced. The whole lightning monitoring system composed of ddw1 lightning location instrument and its networking experiment in Guangdong are introduced. And also it analyzed the consistency between the lightning location data of Guangdong whole lightning monitoring system and the radar data and atmospheric electric field instrument data in this area. When the atmospheric electric field changes violently, the data of lightning monitoring network gathers in this area. Radar data and atmospheric electric field data are usually used for lightning early warning in China. The analysis shows that lightning monitoring data can be used for lightning early warning at the same time as radar data and electric field data. To some extent, it makes up for the shortcomings of wide range of radar data and small range of electric field data. In order to improve the ability of near early warning and the observation of severe convective weather, it is necessary to integrate ground observation technology and space-based detection technology, which is also the development trend of lightning early warning and the direction of fusing data application.
Construction of Chinese Interlanguage Corpus in the Era of Big Data

Under the background of the new era, Chinese language teaching has attracted much attention. Among them, the development of Chinese international education is faster and faster. Few teachers and students stand in a macro perspective, based on the actual conditions of Chinese International Education and teaching, and conduct in-depth analysis and Research on the current situation and form of teaching in this field. The knowledge of Chinese interlanguage corpus is seriously lacking. Although our country has built a domestic interlanguage corpus, most of the knowledge and content have some obstacles and difficulties in application, and there is a certain gap between the quality and level of the substance and the objectives of the previous work. It is difficult to conduct comprehensive analysis and research from an interdisciplinary perspective, which provides a certain basis for the construction of learners’ historical corpus. Compared with other corpora, the construction of interlanguage corpora is more complex and diverse, with obvious nonlinear and dynamic characteristics. In the process of practical operation, every staff member needs to combine theoretical analysis with practical research to understand the essential requirements of the construction of Chinese interlanguage corpus under the background of big data era. It is also necessary to build a perfect management system based on different working principles such as historicity, universality, authenticity, barrier free sharing in and out of class, interdisciplinary and all media.
Analysis of Image Quality using Sobel Filter

Filters can be used to assign new value to a pixel by making use of the local neighbors. This will reduce the noise by smoothing operation by retaining the edges. The method of edge detection is to study the changes in image pixels using the variations of the edge neighboring pixels. In computing changes in these pixel values, selection of the proper window size is essential. In this paper, an attempt is made to implement Sobel filtering technique over Landsat 7 ETM+ satellite data product in order to analyze the impact of bandwidth. The recommendation of window is performed based on the analysis of statistics which best enhances the image while preserving the edges. For satellite image with spatial resolution around 30m, window size 3×3 for SD = 33.527, Mean = 63.265 and SNR = 1.887 is recommended. If results in blurred image, the largest window size 9×9 was recommended to obtain improvement in results.
Early Detection of Hemiplegia by Analyzing the Gait Characteristics and Walking Patterns Using Convolutional Neural Networks

Over the time the human posture can become a cause of concern. Bad posture cannot only be caused by our living habits but can also be a symptom of neurological diseases. Various diseases can be detected and diagnosed at an early stage if the posture pattern of a person is observed and analyzed. In this paper, we devise a method utilizing deep learning and convolutional neural networks which analyzes the gait characteristics of humans to identify Hemiplegia. The method distinguishes the users as healthy or Hemiplegic based on their posture and walking pattern. The proposed method will help health professionals with the clinical examination of a patient for Hemiplegia identifications.
Hybrid Feature Vector Generation for Alzheimer’s Disease Diagnosis Using MRI Images

In the advent of computer-aided diagnosis (CAD), analysis of medical images with the help of various feature extraction techniques is gaining a lot of interest. Nowadays, a lot of research is done in the analysis of neuro-images, particularly the MRI modality to study the structural changes or atrophy of the brain due to various neurological disorders. Alzheimer's disease (AD) being one such illness, affects a vast proportion of elderly populations worldwide. The current diagnosis of AD which is done manually by a clinician using neuro-images can sometimes be inaccurate. Although image processing and machine learning have facilitated feature extraction and diagnosis of AD respectively, there are still many challenges that exist in the current systems. This work proposes a system that will diagnose AD using MRI images by generating a hybrid feature vector (HFV). The HFV is created from fusing features obtained from different techniques such as volumetric analysis of grey matter (GM) and white matter (WM), surface-based cortical thickness and tissue volumetric study of regions of interest (ROI) acquired from Voxel Based Morphometry (VBM) analysis. Machine learning techniques are later used to classify patients (AD) from normal controls (NC). The experimentation of the proposed system was carried out using the T1-weighted MRI scans of 100 subjects (50 AD and 50 NC) obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The maximum accuracy achieved by the proposed system is 93% using the SVM classifier which validates the promising performance of the HFV in AD diagnosis.
Physical Computing in Library and Information Science Master’s Program Curriculum: A Pilot Course Offering and Future Possibilities

Informal library practitioner publications have detailed the use of low-cost physical computing devices (such as micro-controllers and single-board computers) and sensors in broad library applications, such as: running digital signage, hosting OPAC stations, assessing space crowdedness, or automating reference statistics collection. Though physical computing topics have an increasing presence in makerspace-related curriculum within ALA-accredited library and information science Master’s programs, there is a lack of general-purpose coverage suitable to tackling novel problems in the information professions, outside of the makerspace context. This paper presents the development of a Masters-level course in physical computing and rapid prototyping, considers its suitability to wider iSchool curriculum, and details future work in this area.
Clusteling Methods Using Synchronization of Chaotic Circuit Networks

In our previous studies, we have investigated clustering phenomena observed from several types of coupled chaotic networks. In this study, an approach for clustering on complex networks by using synchronization phenomena of coupled chaotic circuits is proposed. We introduce three major chaotic circuit networks and their clustering patterns depending on the chaotic circuits and the network structures.
Refining Algebraic Hierarchy in Mathematical Repository of Mizar

Mathematics, especially algebra, uses plenty of structures: groups, rings, integral domains, fields, vector spaces to name a few of the most basic ones. Classes of structures are closely connected—usually by inclusion—naturally leading to hierarchies that has been reproduced in different forms in different mathematical repositories. We give a brief overview of some existing algebraic hierarchies and report on the latest developments in the Mizar computerized proof assistant system. In particular we present a detailed algebraic hierarchy that has been defined in Mizar and discuss extensions of the hierarchy towards more involved domains, using internal mechanisms available in the system.
The Construction and Measure Method of Dependency Parsing Tree Model

Requirements analysis is the first point of information system development, which has a significant impact on the development. For the requirements of natural language description, automated requirement checking model cannot feasible. To verify the consistency of information system requirements, the paper builds a semantic model with tree nodes of natural language clauses. The model divides clauses into a representation of keywords set with seven-tuple. The paper not only proposed a dependency tree model to solve the problem that the refined tree cannot characterize the relationship between syntactic structure and keywords, but also put forward a dependency tagging algorithm and an algorithm to construct and update dependency parsing tree. The paper further put forward a semantic similarity calculation method to determine similarity among sub clause syntactic structures.
Poster: Design of PBL Educational Program in Collaboration with Printing Company

To educate engineers, we need a method for social implementation education. Therefore, educational innovation including social cooperation has started in Japan. Under this program, students are asked to identify social issues and develop solutions. This paper describes the social implementation project utilized with fourth-year students of International College of Technology, Kanazawa.
Security of Auditing Protocols Against Subversion Attacks

In 2013, the revelation of Edward Snowden rekindled cryptographic researchers’ interest in subversion attacks. Since then, many works have been carried out to explore the power of subversion attacks and feasible effective countermeasures as well. In this work, we investigate the study of subversion attacks against cloud auditing protocol, which has been well-known as useful primitive for secure cloud storage. We demonstrate that subverted auditing protocol enables the cloud server to recover secret information stored on the data owner side. Particularly, we first define an asymmetric subversion attack model for auditing protocol. This model serves as the principle for analyzing the undetectability and key recovery of subversion attacks against auditing protocols. We then show a general framework of asymmetric subversion attacks against auditing protocols with index-coefficient challenge. To illustrate the feasibility of our paradigm, several concrete auditing protocols are provided. As a feasible countermeasure, we propose a subversion-resilient auditing protocol with index-coefficient challenge.
Design and Implementation of Burmese Speech Synthesis System Based on HMM-DNN

The research and application of speech synthesis in Chinese and English are widely used. However, most nonuniversal languages have relatively few electronic language resources, and speech synthesis research is lagging behind. Burmese is a type of alphabetic writing, and Burmese belongs to Tibetan-Burmese branch of the Sino-Tibetan language. In order to develop the Burmese speech synthesis application system, this paper studies the Burmese speech waveform synthesis method, designs and implements a HMM-based Burmese speech synthesis baseline system, and based on this, introduces a deep neural network (DNN) to replace the decision tree model of HMM speech synthesis system, thereby improving the acoustic model to improve the quality of speech synthesis. The experimental results show that the baseline system is feasible, and the introduction of DNN speech synthesis system can effectively improve the quality of speech synthesis.
Improvement of Pre-service Teachers’ Professional Competencies Using DAPOA Project-Based Learning

The research objective aims to improve the pre-service teachers' professional competencies using the project-based DAPOA learning model for engineering education. The project-based learning model is important to encourage learners to have knowledge skills and attitude in professional occupation consistent to the 21st century learning skills. The important learning skills consist of life and career skills, learning and innovation skills, information media and technology skills. Moreover, in educating technology and engineering, active learning is necessary to promote students to have essential skills in industry and education sectors. The developed project-based DAPOA learning model is an active learning that can encourage students to have knowledge and skills by searching, working in team, problem solving, communicating and etc. In this paper, the improvement of the teachers' professional competencies using the project-based learning for developing pre-service teachers was proposed and the efficiency of learning and teaching management and internship was evaluated.
FPGA-Based Detection and Tracking System for Surveillance Camera

Tracking moving image is an important task in surveillance system, since there is a continuous change in the location of the objects. Object detection and tracking find applications in autonomous robot navigation, surveillance, and vehicle navigation. Object detection involves obtaining objects in consecutive frames. Object tracking locates the objects to appear over time using a camera. An field programmable gate array (FPGA) includes a number of configurable logic blocks, distributed memory, and hard digital signal processing modules, which has the capability of processing real-time objects. The chapter discusses tracking objects in consecutive frames using an FPGA. Here, the Xilinx ISE software is used to implement algorithm using VHSIC hardware description language (VHDL) and implemented in Spartan 3E FPGA.
Implementation the Protection Method of Data Transmission in Network

This paper proposes to solve the problems of protection of information against unauthorized access by introducing a system that, based on the developed methods of information exchange, performs a completely isolated from clients transmission. It provides a high level of protection, reliability and cryptographic stability of data transmission indicators. The system provides client-side message encryption with one of the crypto-encryption algorithms. The encrypted client message and the key to decrypt it are transmitted to a predefined server. The transmission is then encrypted by another algorithm that is defined on the server according to the method of assigning the encryption algorithm.
A Novel Interacting T-S Fuzzy Multiple Model by Using UKF for Maneuvering Target Tracking

Dynamic models of maneuvering targets in nonlinear systems are usually difficult to be modeled, and inaccurate dynamic model will lead to the poor performance of tracking algorithm. For these problems, in this paper, a novel interacting Takagi-Sugeno (T-S) fuzzy multiple model maneuvering target tracking algorithm by using UKF for parameter identification is proposed (ITS-UKF). The ITS-UKF algorithm uses multiple semantic fuzzy sets to represent the target feature information, and construct a general T-S fuzzy semantic multiple model framework. In the T-S fuzzy semantic multiple model framework, the intersection degree between fuzzy sets is used to estimate the transition probabilities between different fuzzy rules; fuzzy C-regression model clustering (FCRM) is used to adaptively identify the premise parameters. Moreover, the UKF is also used to identify the consequent parameters to improve the performance for nonlinear system. Simulation results show that the performance of ITS-UKF is superior to IMM-EKF (interacting multiple model extended Kalman filter), IMM-UKF (interacting multiple model unscented Kalman filter), particularly, when the target is maneuvered or the model of the target is inaccurate, the ITS-UKF algorithm has better performance.
Performance Comparison of FPGA-based Convolutional Neural Networks by Internal Representations

Reconfigurable Field-Programmable Gate Arrays (FPGAs) have prospects for applying in mobile and wearable electronics. FPGA-based neural networks have strong advantage in energy consumption comparing to another solutions. For further improving of their energy efficiency it is appropriate to study the individual network parameters effect on the entire system performance. By the reason, different internal representations variants of convolutional neural network (CNN) were compared and investigated. The study involves an accuracy parameters analysis with restricted memory for weights and increasing the network depth. Binary parameters were chosen for FPGA implementation as more efficient. Binarized CNN was compared with equal CNN by memory comsuption of weights. In addition, the mathematical problem statement of realizing binarized neural network is considered.
Lightning Clustering-Based Thunderstorm Recognition and Tracking Algorithm and Its Application

The development of thunderstorms has a life cycle, but the occurrence of thunderstorms is not completely independent. There may be several thunderstorms that are close in time and space. If lightings belong to different thunderstorms are treated as the lightings from one storm, it will lead to wrong conclusions. Therefore, in this paper, the amount of and frequency of lightnings are used to identify the occurrence and development of thunderstorms, a nearest neighbor clustering method is studied. This method can be used to distinguish lightnings occurred from different thunderstorms and indicate the occurrence and development of thunderstorms. The lightning clustering algorithm can also be applied to identify and track of thunderstorms. All work finally is realized by map display on the software platform.
The 3D virtual drawing mobile application based on augmented reality using AR-Framework

Abstract This study discussed the visualization system of 3D sketching model based on mobile Augmented Reality (AR) which supports information needed in real environment. The mobile application was made with adding virtual 3D objects to the 2D drawing by applying technology of AR. The visual system assists those who don’t have any specialized skill to be able to draw easily, because 3D model augmented over the drawing through camera of smart phone was added without any marker. This method is the basic combination between dynamic virtual object with the real environment, which nowadays has been developed as the mix reality. As the result on this research, the application can detect the real environment and mark it as a marker for the drawing-place as well. It will become the strategic future research of mix reality or marker less AR, because with this development, virtual world should be more attractive because it has been presented with immersive mixing to the real world.