Miracle-dz/UniFAR-FTUs huggingface dataset:
A Unified Method for Vocal Source Separation From Stereophonic Music Signals

A unified method for separating musical sources, singing voice for example, from stereophonic mixtures is provided. We usually have two observed signals in stereophonic music contents, where more than two instruments are played together. If we regard each instrument as source, this problem becomes an underdetermined source separation problem and cannot be solved by conventional methods, which infers the spatial environment of the downmixing process happens. Instead, source-specific information has been exploited to recover a particular instrumental source. This paper provides a unifying structure consists of heterogenious ad-hoc separate algorithms, which are designed for separating vocal sources using stereophonic channel information and dominant pitch information of the sources, respectively. Experiments on real world music contents show that the proposed unification can neutralize the drawbacks of the two ad-hoc separation algorithms and finally enhance the separation results.
An Active Control Method for Chatter Suppression in Thin Plate Turning

This paper presents an active control method, consisting of an adaptive sliding-mode controller (ASMC) and a displacement field reconstruction (DFR) method, for chatter suppression in turning of thin-walled workpieces (such as compressor disks and casings in aircraft engines) where low workpiece stiffness renders machining with potential regenerative chatter. Due to the presence of multi-modal dynamics, variant modal parameters, and measurement difficulties, active chatter control of thin plate turning has been challenging. Unlike existing controls based on a lumped-parameter single degree-of-freedom cutting model, a distributed-parameter dynamic model of a rotating thin plate with multiple vibration modes is used to analyze the machining stability with the designed controller. Moreover, model parameters of the plate are not needed to construct the controller. The DFR is employed to capture the plate dynamic behavior for feedback to the ASMC during turning, overcoming the long existing difficulties to measure plate vibration at the cutting point. A fast tool servo is utilized in the control implementation. Theoretical analyses, numerical simulations, and experimental evaluation on a lathe demonstrate that chatter in thin plate turning can be effectively attenuated with the proposed active control method.
CAD Virtual Human Male Model

The purpose of this MQP has been to create an accurate cross-platform CAD representation of the male human body for the use in electromagnetic computer simulations. The model is to be employed as a substitute for the human body for SAR (Specific Absorption Rate) estimations. An emerging application includes estimations of the heating rate and temperature rise for embedded metallic implants during a Magnetic Resonance Imaging (MRI) procedure. During the MQP project period, we have created a manifold CAD model with 233 individual tissue parts in the form of triangular meshes. All tissue parts are true 2-manifold CAD meshes; these meshes do not intersect. The model is being tested in an MRI RF coil using the finite element method based computational electromagnetics software, ANSYS High Frequency Structural Simulator (HFSS).
Representation Learning for Information Extraction

Distributed representations, predominantly acquired via neural networks, have been applied to natural language processing tasks including speech recognition and machine translation with a success comparable to sophisticated state-of-the-art algorithms. The present thesis offers an investigation of the application of such representations to information extraction. Specifically, I explore the suitability of applying shallow distributed representations to the automatic terminology extraction task, as well as the bridging reference resolution task. I created a dataset as a gold standard for automatic term extraction in the mathematical education domain. I carefully assessed the performance of the existing terminology extraction methods on this dataset. Then, I introduce a novel method %DI it is one method or several? I changed to one method since the next sentence says algorithm at singular not plural %Ehsan: noted, thanks. for automatic terminology extraction for one word terms, and I evaluate the performance of the novel algorithm in various terminological domains. The introduced algorithm leverages the distributed representation of words from the local and global perspectives to encode syntactic, semantic, association, and frequency information at the same time. Furthermore, this novel algorithm can be trained with a minimal number of data points. I show that the algorithm is robust to the change of domain, and that information can be transferred from one technical domain to another, leveraging what we call anchor words with consistent semantics shared between the domains. As for the bridging reference resolution task, a dataset is built on the letter portion of the Open American National Corpus and I compare the performance of a preliminary method against a majority class baseline.
Research of power system online dynamic security assessment application expansion

In this paper, limitations of the online DSA based on the real-time data are analyzed. And thus three kinds of DSA application modes are proposed including real-time analysis mode, intervention analysis mode and trend analysis mode. Concrete research purpose, source data and processing flow of proposed application modes are expounded respectively. Overall work flow of on-line DSA involved proposed application modes is introduced in detail. Coordinated operation of various application modes can avoid the limitations of stability analysis based on real-time data simply. A case in practical operation is presented to illustrate coordinated application modes and prove the significance of DSA application modes expansion. Practical results prove that the coordinated operation can provide effective guarantee for security and stability operation of power grid.
Using System Architecture, Review Entry Criteria, and Standard Work Package Data to Enable Rapid Development of Integrated Master Schedules

Abstract : While engineers must participate in the construction of the Integrated Master Schedule, this thesis proposes a way to reduce that effort through automation. When standardized sub processes exist, automated task name construction with consistent action/object naming convention can be applied to multiple system artifacts. These repeating sub processes also allow the derivation of task sequence and dependencies. The Architecture-Based Utility for Repeating Task Planning (A-BURTP) is a Microsoft Access database constructed of tables containing system configuration items, artifact data, local processes, and governing instructions listed in the systems engineering plan (SEP). A-BURTP generates task planning worksheets (TPWs) in Microsoft Excel that import directly into Microsoft Project. The TPWs contain all task attributes needed to allow schedule developers to work independently to link, sort, and group the imported tasks.Appendices include research on system context for Naval Air Systems Command (NAVAIR) systems onboard aircraft carriers and research on Model-Based Systems Engineering data exports using Vitech CORE9. Scope is limited to constructing the artifact tasking for an Engineering Change Proposal on a fictional system under change. NAVAIR processes and instructions are used when available. The 2011 Office of the Secretary of Defense SEP template is a key reference.
Exploiting domain knowledge to enhance opinion mining using a hybrid semantic knowledgebase-machine learning approach

With the fast growth of World Wide Web 2.0, a great number of opinions about a variety of products have been published on blogs, forums, and social networks. Online opinions play an important role in supporting consumers make decisions about purchasing products or services. In addition, customer reviews allow companies to understand the strengths and limitations of their products and services, which aids in improving their marketing campaigns. The challenge is that online opinions are predominantly expressed in natural language text, and hence opinion mining tools are required to facilitate the effective analysis of opinions from the unstructured text and to allow for qualitative information extraction. This research presents a Hybrid Semantic Knowledgebase-Machine Learning approach for mining opinions at the domain feature level and classifying the overall opinion on a multi-point scale. The proposed approach benefits from the advantages of deploying a novel Semantic Knowledgebase approach to analyse a collection of reviews at the domain feature level and produce a set of structured information that associates the expressed opinions with specific domain features. The information in the knowledgebase is further supplemented with domain-relevant facts sourced from public Semantic datasets, and the enriched semantically-tagged information is then used to infer valuable semantic information about the domain as well as the expressed opinions on the domain features by summarising the overall opinions about the domain across multiple reviews, and by averaging the overall opinions about other cinematic features. The retrieved semantic information represents a valuable resource for training a Machine Learning classifier to predict the numerical rating of each review. Experimental evaluation revealed that the proposed Hybrid Semantic Knowledgebase-Machine Learning approach improved the precision and recall of the extracted domain features, and hence proved suitable for producing an enriched dataset of semantic features that resulted in higher classification accuracy.
Serverless Mobile Multiuser Chat Application Based on ChronoSync for Named Data Networking

Client-server application model is prone to single point of failure on the server, thus making distributed model a good choice against the problem. Named Data Networking (NDN) has been a prospecting architecture to enable the future internet, putting the focus of the network to ‘what’ data to fetch instead of ‘where’ to send the data. NDN can provide communication for many networking devices, including mobile devices. In this paper, we present DChronoChat, an implementation of ChronoSync synchronization protocol to provide a distributed, serverless mobile multiuser chat application on NDN network. We implemented DChronoChat using jNDN 0.10 library on Android devices with real NDN Forwarding Daemon (NFD) for Android device. We show through our implementation that ChronoSync can provide synchronization in real implementation of mobile multiuser chat application.
Resource allocation in heterogeneous cognitive radio sensor networks

Cognitive radio sensor networks offer a promising means of meeting rapidly expanding demand for wireless sensor network applications in new monitoring and objects tracking fields. Several challenges, particularly in terms of quality of service provisioning, arise because of the inherited capability-limitation of end-sensor nodes. In this article, an efficient resource allocation scheme, improved Pliable Cognitive Medium Access Protocol, is proposed to tackle multilevel of heterogeneity in cognitive radio sensor networks. The first level is the network’s application heterogeneity, and the second level is the heterogeneity of the radio environment. The proposed scheme addresses scheduling and radio channel allocation issues. Allocation-decision making is centralized, whereas spectrum sensing is distributed, thereby increasing efficiency and limiting interference. Despite the limited capabilities of the sensor’s networks, the effectiveness of the proposed scheme also includes increasing the opportunity to utilize a wider range of the radio spectrum. improved Pliable Cognitive Medium Access protocol is quite appropriate for critical communications that gain attention in the next 5G of wireless networks. Simulation results and the comparison of the proposed protocol with other protocols indicate the robust performance of the proposed scheme. The results reveal the significant effectiveness, with only a slight trade-off in terms of complexity.
A comparison of integration architectures

A Comparison of Integration Architectures by Amar Mahidadia This paper presents GenSIF, a Generic Systems Integration Framework. GenSIF features a pre-planned development process on a domain-wide basis and facilitates system integration and project coordination for very large, complex and distributed systems. Domain analysis, integration architecture design and infrastructure design are identified as the three main components of GenSIF. In the next step we map Bellcore' s OSCA interoperability architecture, ANSA, IBM's SAA and Bull's DCM into GenSIF. Using the GenSIF concepts we compare each of these architectures. GenSIF serves as a general framework to evaluate and position specific architecture. The OSCA architecture is used to discuss the impact of vendor architectures on application development. All opinions expressed in this paper, especially with regard to the OSCA architecture, are the opinions of the author and do not necessarily reflect the point of view of any of the mentioned companies. A COMPARISON OF INTEGRATION ARCHITECTURES
An Approach of Latent Semantic Space Partition and Web Document Clustering

This paper applies the LDA model to analyze latent semantics of documents and partition the semantic space into low,middle and high frequency space.The semantics in low frequency space are used to detect outlier web documents.The semantics in middle and high frequency space are devoted to document clustering as features of the documents.The quality of clustering results is improved by a mutual-action mechanism between document clusters and semantics.Compared with related work,this paper not only applies LDA model to represent documents,but also analyzes the semantic distribution in depth and applies the results of analysis to web document clustering.Experiments show that the clustering algorithm of the mutual-action between LDA-based document class and semantic in this paper deserve better effects in document clustering.
Scholarship Grants Prediction using Autoregressive Integrated Moving Average (ARIMA) Algorithm

Every university has its methods to realize scholarship assessment. Schools establish scholarships as a motivation for students that have outstanding and exceptional achievements. It is a form of incentive and benefit to inspire and embolden students and improve schools’ learning principles. From that point of view, an optimal way is considered to assess and distribute scholarship grants. In this paper, the use of Autoregressive Integrated Moving Average (ARIMA) was observed in predicting future scholarship grants that can be availed in Surigao State College of Technology, Surigao City, Philippines. The output of this study can be used as input for management decision as to budget allocation by the sponsoring agent of the scholarship program. It is also hoped that this study could contribute on the literature of time series forecasting and educational data mining in general.
Incidentally Detected Breast Lesions on Computed Tomography: Comparison with Mammography and Ultrasound

Objectives: To determine the reliability of incidentally detected breast lesion on CT scan compared with mammography and ultrasound. Methods: The study group is consisted of 39 patients (49 breast lesions), who incidentally found breast lesions from chest or abdomen CT scan. The study was retrospectively reviewed CT features (size, shape, margin, density and enhancement pattern) and mammography with ultrasound features and categorized into American College of Radiology Breast Imaging Reporting And Data System (ACR BI-RADS). The comparison of ACR BI-RADS from CT scan and mammography with ultrasound was made by Kappa test to evaluate the agreement. Results: There was substantial agreement (k = 0.741, 95% CI 0.594-0.887) between CT and mammography with supplementary ultrasound findings. Mass lesions were found 46 in 49 lesions, macrocalcifications without mass 2 lesions and non-mass lesion 1 lesion (total 49 lesions). Most common shape of breast lesion from CT scan was oval shape seen 36 in 46 lesions ( 78.3%). Most common margin was circumscribed seen 38 in 46 lesions (82.6%). Iso-density lesions were seen 18 in 26 lesions ( 69.3%), 3 hyperdense lesions (11.5%) and 4 hypodense dense lesions ( 15.4%) and one fat-containing lesion ( 3.8%). Hounsfield unit (HU) study is between -50 to 61 HU ( mean 27.1). Homogeneous enhancement were seen 27 in 44 lesions ( 61.4%) , non-enhancement pattern of 12 lesions (27.2%). Four non-enhance lesions were recognized as cysts due to low attenuation (< 20 HU) There were 11 lesions containing internal calcification, most of which were macrocalcification seen 9 in 11 lesions ( 81.8%). Overlying skin and nipple involvement were observed 2 in 49 lesions ( 4.1%). Lymphadenopathy and chest wall invasion were found in one case ( 2.0%). Conclusions: CT was rather good for evalulation of shape and margin of breast masses, while density value, enhancement pattern and calcification show non-specific results. ). Moreover, contrast enhanced CT scan has rather good reliability for diagnosis of benign lesion (BI-RADS 2) and malignant lesion (BI-RADS 5). However, for interpretation of suspicious lesion (BI-RADS 4) , it should be interpreted cautiously and compared with mammography and ultrasound due to over diagnosis in CT in this group.
A System for Decoding and Coloring Arabic Text for Language Learners

Language learners face difficulties while reading and comprehending Arabic text, this is because of the interwoven nature of Arabic script. In this paper, we present (Arcode) an automatic web-based system, which simplifies instruction on Arabic word decoding and comprehension through utilizing color-coding on Arabic text. The system is designed to help Arabic language learners to analyze and identify attached affixes (prefixes & suffixes), particle and silent letters of the selected text, and sentences based on the sentence morphological structure. This is done by encoding and presenting color conversion per word and character, using a certain color code. The proposed system is a combination of color-coding technique, transliteration, and text-to-speech technologies that creates an educational tool for learning the Arabic language. It also provides web services for developers who want to integrate the system into their own applications.
Threshold-Free Physical Layer Authentication Based on Machine Learning for Industrial Wireless CPS

Wireless industrial cyber-physical systems are increasingly popular in critical manufacturing processes. These kinds of systems, besides high performance, require strong security and are constrained by low computational capabilities. Physical layer authentication (PHY-AUC) is a promising solution to meet these requirements. However, the existing threshold-based PHY-AUC methods only perform ideally in stationary scenarios. To improve the performance of PHY-AUC in mobile scenarios, this article proposes a novel threshold-free PHY-AUC method based on machine learning (ML), which replaces the traditional threshold-based decision-making with more adaptive classification based on ML. This article adopts channel matrices estimated by the wireless nodes as the authentication input and investigates the optimal dimension of the channel matrices to further improve the authentication accuracy without increasing too much computational burden. Extensive simulations are conducted based on a real industrial dataset, with the aim of tuning the authentication performance, then further field validations are performed in an industrial factory. The results from both the simulations and validations show that the proposed method significantly improves the authentication accuracy.
Reliability assessment method for geographically distributed information systems

this research defines reliability assessment method for information systems (hereinafter IS) in geographically distributed organizations. The article starts with the description of reliability indicators and general task formulation. The approach towards the design of a mathematical reliability model for geographically distributed systems based on the reliability scheme in the form of a graph is validated. There is the calculation of reliability indicators for a 3-level hierarchical system in the article. Advantage of the presented model is that it is suitable for both centralized and distributed information systems. In conclusion, there are some examples of implementation of the suggested approach in three geographically distributed information systems and comparison with the similar studies.
Push the Limit of PIR Sensor based Localization

Pyroelectric infrared (PIR) sensors are considered to be promising devices for device-free localization due to its advantages of low cost, less intrusive, and the immunity from multi-path fading. However, most of the existing PIR-based localization systems only utilize the binary information of PIR sensors and therefore require a large number of PIR sensors and a careful deployment. A few works directly map the raw data of PIR sensors to one's location using machine learning approaches. However, these approaches require to collect abundant training data and suffer from environmental change. In this paper, we propose a PIR-based device-free localization approach based on the raw data of PIR sensors. The key of this approach is to extract a new type of location information called as the azimuth change. The extraction of the azimuth change relies on the physical properties of PIR sensors. Therefore, no abundant training data are needed and the system is robust to environmental change. Through experiments, we demonstrated that a device-free localization system incorporating the information of azimuth change outperforms the state-of-the-art approaches in terms of higher location accuracy. In addition, the information of the azimuth change can be easily integrated with other PIR-based localization systems to improve their localization accuracy.
Key-Attributes-Based Ensemble Classifier for Customer Churn Prediction

Recently, it has been seen that the ensemble classifier is an effective way to enhance the prediction performance. However, it usually suffers from the problem of how to construct an appropriate classifier based on a set of complex data, for example, the data with many dimensions or hierarchical attributes. This study proposes a method to constructe an ensemble classifier based on the key attributes. In addition to its high-performance on precision shared by common ensemble classifiers, the calculation results are highly intelligible and thus easy for understanding. Furthermore, the experimental results based on the real data collected from China Mobile show that the key-attributes-based ensemble classifier has the good performance on both of the classifier construction and the customer churn prediction.
Java Bytecode Optimization Framework

Soot can be used as an independent tool to optimize or inspect Java bytecode,as well as a framework to develop optimizations or transformations on bytecode. In order to guide users to work with Soot fleetly,the main data structures and flow of Soot and the methodologies of the optimization framework are summed up. An example is given to anatomize the implementation mechanisms of Soot. Extension steps of Soot and related application are stated.